Overview

Brought to you by YData

Dataset statistics

Number of variables47
Number of observations192
Missing cells1254
Missing cells (%)13.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory424.1 KiB
Average record size in memory2.2 KiB

Variable types

Numeric2
Text12
DateTime3
Categorical29
Unsupported1

Alerts

Thrombus has constant value "1.0" Constant
1 = Fokussuche, 2 = Ausschluss/Nachweis Diszitis weil MRT unklar, 3 = MRT nicht möglich / Ersatz für MRT, 4 = VK, 5 = Materialschaden, Frage nach Infekt, 6 = Infekt im Labor ohne Fokus is highly overall correlated with discitis in MRT = TE, 2 Frage diszitis b MRT unklar, 0 = n übereinstimmend, 3 kein MRT, 4 Ausschluss Diszitis, 5 Diszitis im MRT n erkannt, 6 neuer Nachweis Diszitis and 1 other fieldsHigh correlation
1 = lowgrade 2 = highgrade is highly overall correlated with Unnamed: 0 and 1 other fieldsHigh correlation
Author: MRT: keine Diszitis, PET Diszitis spinal: overall, 0 = nicht übereinstimmend, 1= übereinstimmend, 2 = MRT unklar, 3 kein MRT, 4 unklar is highly overall correlated with discitis in MRT = TE, 2 Frage diszitis b MRT unklar, 0 = n übereinstimmend, 3 kein MRT, 4 Ausschluss Diszitis, 5 Diszitis im MRT n erkannt, 6 neuer Nachweis DiszitisHigh correlation
Fokus abgeklärt is highly overall correlated with add TEHigh correlation
Unnamed: 0 is highly overall correlated with 1 = lowgrade 2 = highgradeHigh correlation
add TE is highly overall correlated with Fokus abgeklärt and 1 other fieldsHigh correlation
add TE 1 = new focus 2=TE nicht relevant und tatsächlich nicht relevant 3 = nicht untersucht 4 = kein Fokus 5 = bekannter Fokus, schon behandelt 6 Tumor 0 no add TE is highly overall correlated with add TE and 1 other fieldsHigh correlation
discitis in MRT = TE, 2 Frage diszitis b MRT unklar, 0 = n übereinstimmend, 3 kein MRT, 4 Ausschluss Diszitis, 5 Diszitis im MRT n erkannt, 6 neuer Nachweis Diszitis is highly overall correlated with 1 = Fokussuche, 2 = Ausschluss/Nachweis Diszitis weil MRT unklar, 3 = MRT nicht möglich / Ersatz für MRT, 4 = VK, 5 = Materialschaden, Frage nach Infekt, 6 = Infekt im Labor ohne Fokus and 2 other fieldsHigh correlation
nonspinal: overall 2 unklar 3 nicht abklärt 0 nicht übereinstimmend 4 ausgeheilt nach Behandlung 5 nicht abgebildet is highly overall correlated with add TE 1 = new focus 2=TE nicht relevant und tatsächlich nicht relevant 3 = nicht untersucht 4 = kein Fokus 5 = bekannter Fokus, schon behandelt 6 Tumor 0 no add TEHigh correlation
reason for PET is highly overall correlated with 1 = Fokussuche, 2 = Ausschluss/Nachweis Diszitis weil MRT unklar, 3 = MRT nicht möglich / Ersatz für MRT, 4 = VK, 5 = Materialschaden, Frage nach Infekt, 6 = Infekt im Labor ohne Fokus and 2 other fieldsHigh correlation
unspez Fokus abgeklärt 0nein 1ja+neg 2ja+pos is highly overall correlated with unspez gewertetHigh correlation
unspez gewertet is highly overall correlated with unspez Fokus abgeklärt 0nein 1ja+neg 2ja+posHigh correlation
Fokus abgeklärt is highly imbalanced (59.0%) Imbalance
unspez gewertet is highly imbalanced (82.1%) Imbalance
unspez Fokus abgeklärt 0nein 1ja+neg 2ja+pos is highly imbalanced (77.7%) Imbalance
weitere is highly imbalanced (84.7%) Imbalance
biopsy is highly imbalanced (51.0%) Imbalance
histo surgery 3 intermediär 0 neg is highly imbalanced (53.8%) Imbalance
TE (at all) is highly imbalanced (55.8%) Imbalance
discitis in MRT = TE, 2 Frage diszitis b MRT unklar, 0 = n übereinstimmend, 3 kein MRT, 4 Ausschluss Diszitis, 5 Diszitis im MRT n erkannt, 6 neuer Nachweis Diszitis is highly imbalanced (55.5%) Imbalance
other spinal TE is highly imbalanced (83.5%) Imbalance
Author: MRT: keine Diszitis, PET Diszitis spinal: overall, 0 = nicht übereinstimmend, 1= übereinstimmend, 2 = MRT unklar, 3 kein MRT, 4 unklar is highly imbalanced (55.0%) Imbalance
add TE 1 = new focus 2=TE nicht relevant und tatsächlich nicht relevant 3 = nicht untersucht 4 = kein Fokus 5 = bekannter Fokus, schon behandelt 6 Tumor 0 no add TE is highly imbalanced (54.3%) Imbalance
nonspinal: overall 2 unklar 3 nicht abklärt 0 nicht übereinstimmend 4 ausgeheilt nach Behandlung 5 nicht abgebildet is highly imbalanced (65.3%) Imbalance
reason for PET is highly imbalanced (62.6%) Imbalance
1 = Fokussuche, 2 = Ausschluss/Nachweis Diszitis weil MRT unklar, 3 = MRT nicht möglich / Ersatz für MRT, 4 = VK, 5 = Materialschaden, Frage nach Infekt, 6 = Infekt im Labor ohne Fokus is highly imbalanced (59.7%) Imbalance
RevisionsOP 2 =kein Infekt is highly imbalanced (68.8%) Imbalance
weitere has 2 (1.0%) missing values Missing
Thrombus has 177 (92.2%) missing values Missing
OP has 3 (1.6%) missing values Missing
surgery date has 16 (8.3%) missing values Missing
date of PET has 6 (3.1%) missing values Missing
1 = lowgrade 2 = highgrade has 54 (28.1%) missing values Missing
mibi other has 11 (5.7%) missing values Missing
PETCT TE has 192 (100.0%) missing values Missing
Neurologie 1 = Paresen, 2 = vorbestehend, 3 = Tetraparese has 122 (63.5%) missing values Missing
Besserung 1 = komplett 2 =ja, aber nicht auf normal 3 tot 4 neues Defizit has 128 (66.7%) missing values Missing
(1= DM, 2=iv Drogen, 3=PAVK, 4=Cortisontherapie, 5=Zahnbehandlung, 6=Immunschwäche, 7=zn CTX, 8=Malignom, 9=Infiltrationen, 10=C2, 11=Parkinson, 12 = Niereninsuffizienz) has 180 (93.8%) missing values Missing
Unnamed: 45 has 171 (89.1%) missing values Missing
Unnamed: 46 has 186 (96.9%) missing values Missing
PETCT TE is an unsupported type, check if it needs cleaning or further analysis Unsupported

Reproduction

Analysis started2025-11-23 12:47:46.943006
Analysis finished2025-11-23 12:47:53.503175
Duration6.56 seconds
Software versionydata-profiling vv4.16.0
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

High correlation 

Distinct191
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2514839.6
Minimum1025571
Maximum21655353
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2025-11-23T13:47:53.600717image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1025571
5-th percentile1248864.6
Q11971637.5
median2729249
Q32919676.8
95-th percentile3022488.8
Maximum21655353
Range20629782
Interquartile range (IQR)948039.25

Descriptive statistics

Standard deviation1520342.2
Coefficient of variation (CV)0.60454838
Kurtosis132.76112
Mean2514839.6
Median Absolute Deviation (MAD)265960.5
Skewness10.466038
Sum4.8284921 × 108
Variance2.3114405 × 1012
MonotonicityNot monotonic
2025-11-23T13:47:53.737484image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2774956 2
 
1.0%
1069341 1
 
0.5%
1427060 1
 
0.5%
2801175 1
 
0.5%
2817925 1
 
0.5%
2872465 1
 
0.5%
2876167 1
 
0.5%
2863953 1
 
0.5%
2874820 1
 
0.5%
2872382 1
 
0.5%
Other values (181) 181
94.3%
ValueCountFrequency (%)
1025571 1
0.5%
1050923 1
0.5%
1069341 1
0.5%
1086829 1
0.5%
1110893 1
0.5%
1121310 1
0.5%
1174643 1
0.5%
1187375 1
0.5%
1202379 1
0.5%
1227623 1
0.5%
ValueCountFrequency (%)
21655353 1
0.5%
3052507 1
0.5%
3049199 1
0.5%
3047471 1
0.5%
3041219 1
0.5%
3037105 1
0.5%
3030845 1
0.5%
3030539 1
0.5%
3029294 1
0.5%
3026847 1
0.5%

name
Text

Distinct191
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Memory size16.0 KiB
2025-11-23T13:47:53.965095image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length33
Median length25
Mean length16.296875
Min length9

Characters and Unicode

Total characters3129
Distinct characters60
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique190 ?
Unique (%)99.0%

Sample

1st rowRankel, Christine
2nd rowMentzel, Frank
3rd rowVial, Renee
4th rowMayr, Daniela
5th rowZellner, Leonhard
ValueCountFrequency (%)
peter 9
 
2.2%
rudolf 6
 
1.5%
franz 5
 
1.2%
wolfgang 5
 
1.2%
fischer 5
 
1.2%
ingrid 4
 
1.0%
dr 4
 
1.0%
maria 4
 
1.0%
helmut 4
 
1.0%
gerhard 3
 
0.7%
Other values (315) 354
87.8%
2025-11-23T13:47:54.293838image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 337
 
10.8%
r 278
 
8.9%
a 226
 
7.2%
211
 
6.7%
, 192
 
6.1%
i 192
 
6.1%
n 170
 
5.4%
l 163
 
5.2%
t 112
 
3.6%
h 101
 
3.2%
Other values (50) 1147
36.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2289
73.2%
Uppercase Letter 416
 
13.3%
Space Separator 211
 
6.7%
Other Punctuation 199
 
6.4%
Dash Punctuation 14
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 337
14.7%
r 278
12.1%
a 226
9.9%
i 192
 
8.4%
n 170
 
7.4%
l 163
 
7.1%
t 112
 
4.9%
h 101
 
4.4%
d 87
 
3.8%
o 86
 
3.8%
Other values (20) 537
23.5%
Uppercase Letter
ValueCountFrequency (%)
H 47
 
11.3%
S 42
 
10.1%
M 35
 
8.4%
G 28
 
6.7%
R 25
 
6.0%
B 24
 
5.8%
A 23
 
5.5%
K 23
 
5.5%
F 19
 
4.6%
D 19
 
4.6%
Other values (14) 131
31.5%
Other Punctuation
ValueCountFrequency (%)
, 192
96.5%
. 5
 
2.5%
: 1
 
0.5%
? 1
 
0.5%
Space Separator
ValueCountFrequency (%)
211
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2705
86.4%
Common 424
 
13.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 337
 
12.5%
r 278
 
10.3%
a 226
 
8.4%
i 192
 
7.1%
n 170
 
6.3%
l 163
 
6.0%
t 112
 
4.1%
h 101
 
3.7%
d 87
 
3.2%
o 86
 
3.2%
Other values (44) 953
35.2%
Common
ValueCountFrequency (%)
211
49.8%
, 192
45.3%
- 14
 
3.3%
. 5
 
1.2%
: 1
 
0.2%
? 1
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3110
99.4%
None 19
 
0.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 337
 
10.8%
r 278
 
8.9%
a 226
 
7.3%
211
 
6.8%
, 192
 
6.2%
i 192
 
6.2%
n 170
 
5.5%
l 163
 
5.2%
t 112
 
3.6%
h 101
 
3.2%
Other values (46) 1128
36.3%
None
ValueCountFrequency (%)
ü 8
42.1%
ö 7
36.8%
ä 3
 
15.8%
ß 1
 
5.3%

DOB
Date

Distinct191
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
Minimum1930-12-05 00:00:00
Maximum1994-11-11 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-11-23T13:47:54.421290image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T13:47:54.567969image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

age
Real number (ℝ)

Distinct54
Distinct (%)28.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68.895833
Minimum24
Maximum92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2025-11-23T13:47:54.708501image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum24
5-th percentile40.55
Q161
median71
Q379.25
95-th percentile85
Maximum92
Range68
Interquartile range (IQR)18.25

Descriptive statistics

Standard deviation13.311397
Coefficient of variation (CV)0.19321047
Kurtosis0.73779381
Mean68.895833
Median Absolute Deviation (MAD)9
Skewness-0.93220664
Sum13228
Variance177.19328
MonotonicityNot monotonic
2025-11-23T13:47:54.845196image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
67 9
 
4.7%
81 9
 
4.7%
85 9
 
4.7%
75 8
 
4.2%
82 8
 
4.2%
78 7
 
3.6%
69 7
 
3.6%
71 7
 
3.6%
77 7
 
3.6%
74 6
 
3.1%
Other values (44) 115
59.9%
ValueCountFrequency (%)
24 1
0.5%
27 1
0.5%
32 1
0.5%
34 1
0.5%
36 1
0.5%
37 1
0.5%
38 1
0.5%
39 2
1.0%
40 1
0.5%
41 1
0.5%
ValueCountFrequency (%)
92 1
 
0.5%
91 1
 
0.5%
88 1
 
0.5%
87 2
 
1.0%
86 2
 
1.0%
85 9
4.7%
84 6
3.1%
83 3
 
1.6%
82 8
4.2%
81 9
4.7%

Fokus abgeklärt
Categorical

High correlation  Imbalance 

Distinct7
Distinct (%)3.7%
Missing1
Missing (%)0.5%
Memory size12.6 KiB
-
133 
1
50 
Author: abgelehnt 0
 
3
2
 
2
Author: Patient zu schlecht 0
 
1
Other values (2)
 
2

Length

Max length83
Median length1
Mean length1.9842932
Min length1

Characters and Unicode

Total characters379
Distinct characters35
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)1.6%

Sample

1st row-
2nd row-
3rd row-
4th row-
5th row-

Common Values

ValueCountFrequency (%)
- 133
69.3%
1 50
 
26.0%
Author: abgelehnt 0 3
 
1.6%
2 2
 
1.0%
Author: Patient zu schlecht 0 1
 
0.5%
Author: antibiotische Therapie weiter bei fallendem CRP, TCH ohne Handlungsbedarf 1 1
 
0.5%
Author: Pat. verstorben 0 1
 
0.5%
(Missing) 1
 
0.5%

Length

2025-11-23T13:47:54.970440image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-23T13:47:55.062217image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
133
62.1%
1 51
 
23.8%
author 6
 
2.8%
0 5
 
2.3%
abgelehnt 3
 
1.4%
2 2
 
0.9%
fallendem 1
 
0.5%
pat 1
 
0.5%
handlungsbedarf 1
 
0.5%
ohne 1
 
0.5%
Other values (10) 10
 
4.7%

Most occurring characters

ValueCountFrequency (%)
- 133
35.1%
1 51
 
13.5%
23
 
6.1%
e 20
 
5.3%
t 17
 
4.5%
h 14
 
3.7%
r 11
 
2.9%
a 10
 
2.6%
n 10
 
2.6%
o 9
 
2.4%
Other values (25) 81
21.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 141
37.2%
Dash Punctuation 133
35.1%
Decimal Number 58
15.3%
Space Separator 23
 
6.1%
Uppercase Letter 16
 
4.2%
Other Punctuation 8
 
2.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 20
14.2%
t 17
12.1%
h 14
9.9%
r 11
 
7.8%
a 10
 
7.1%
n 10
 
7.1%
o 9
 
6.4%
u 8
 
5.7%
i 7
 
5.0%
b 7
 
5.0%
Other values (11) 28
19.9%
Uppercase Letter
ValueCountFrequency (%)
A 6
37.5%
P 3
18.8%
H 2
 
12.5%
C 2
 
12.5%
T 2
 
12.5%
R 1
 
6.2%
Decimal Number
ValueCountFrequency (%)
1 51
87.9%
0 5
 
8.6%
2 2
 
3.4%
Other Punctuation
ValueCountFrequency (%)
: 6
75.0%
, 1
 
12.5%
. 1
 
12.5%
Dash Punctuation
ValueCountFrequency (%)
- 133
100.0%
Space Separator
ValueCountFrequency (%)
23
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 222
58.6%
Latin 157
41.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 20
12.7%
t 17
 
10.8%
h 14
 
8.9%
r 11
 
7.0%
a 10
 
6.4%
n 10
 
6.4%
o 9
 
5.7%
u 8
 
5.1%
i 7
 
4.5%
b 7
 
4.5%
Other values (17) 44
28.0%
Common
ValueCountFrequency (%)
- 133
59.9%
1 51
 
23.0%
23
 
10.4%
: 6
 
2.7%
0 5
 
2.3%
2 2
 
0.9%
, 1
 
0.5%
. 1
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 379
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 133
35.1%
1 51
 
13.5%
23
 
6.1%
e 20
 
5.3%
t 17
 
4.5%
h 14
 
3.7%
r 11
 
2.9%
a 10
 
2.6%
n 10
 
2.6%
o 9
 
2.4%
Other values (25) 81
21.4%

unspez gewertet
Categorical

High correlation  Imbalance 

Distinct13
Distinct (%)6.8%
Missing1
Missing (%)0.5%
Memory size12.6 KiB
-
176 
Colitis
 
4
HSM
 
1
kleine Gelenke, ae rheumatologisch
 
1
Pneumonie
 
1
Other values (8)
 
8

Length

Max length34
Median length1
Mean length1.7172775
Min length1

Characters and Unicode

Total characters328
Distinct characters36
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)5.8%

Sample

1st row-
2nd row-
3rd row-
4th row-
5th row-

Common Values

ValueCountFrequency (%)
- 176
91.7%
Colitis 4
 
2.1%
HSM 1
 
0.5%
kleine Gelenke, ae rheumatologisch 1
 
0.5%
Pneumonie 1
 
0.5%
AC Gelenk 1
 
0.5%
Magen 1
 
0.5%
Sigmoiditis 1
 
0.5%
Schulter 1
 
0.5%
Dekubitus Knöchel 1
 
0.5%
Other values (3) 3
 
1.6%

Length

2025-11-23T13:47:55.191270image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
176
89.8%
colitis 4
 
2.0%
schulter/becken 1
 
0.5%
hüft-tep 1
 
0.5%
knöchel 1
 
0.5%
dekubitus 1
 
0.5%
schulter 1
 
0.5%
sigmoiditis 1
 
0.5%
magen 1
 
0.5%
gelenk 1
 
0.5%
Other values (8) 8
 
4.1%

Most occurring characters

ValueCountFrequency (%)
- 177
54.0%
e 18
 
5.5%
i 16
 
4.9%
l 12
 
3.7%
o 10
 
3.0%
t 10
 
3.0%
n 9
 
2.7%
s 7
 
2.1%
u 6
 
1.8%
C 6
 
1.8%
Other values (26) 57
 
17.4%

Most occurring categories

ValueCountFrequency (%)
Dash Punctuation 177
54.0%
Lowercase Letter 120
36.6%
Uppercase Letter 24
 
7.3%
Space Separator 5
 
1.5%
Other Punctuation 2
 
0.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 18
15.0%
i 16
13.3%
l 12
10.0%
o 10
8.3%
t 10
8.3%
n 9
7.5%
s 7
 
5.8%
u 6
 
5.0%
c 5
 
4.2%
h 5
 
4.2%
Other values (10) 22
18.3%
Uppercase Letter
ValueCountFrequency (%)
C 6
25.0%
S 4
16.7%
M 2
 
8.3%
H 2
 
8.3%
P 2
 
8.3%
G 2
 
8.3%
E 1
 
4.2%
T 1
 
4.2%
A 1
 
4.2%
K 1
 
4.2%
Other values (2) 2
 
8.3%
Other Punctuation
ValueCountFrequency (%)
/ 1
50.0%
, 1
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 177
100.0%
Space Separator
ValueCountFrequency (%)
5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 184
56.1%
Latin 144
43.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 18
 
12.5%
i 16
 
11.1%
l 12
 
8.3%
o 10
 
6.9%
t 10
 
6.9%
n 9
 
6.2%
s 7
 
4.9%
u 6
 
4.2%
C 6
 
4.2%
c 5
 
3.5%
Other values (22) 45
31.2%
Common
ValueCountFrequency (%)
- 177
96.2%
5
 
2.7%
/ 1
 
0.5%
, 1
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 326
99.4%
None 2
 
0.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 177
54.3%
e 18
 
5.5%
i 16
 
4.9%
l 12
 
3.7%
o 10
 
3.1%
t 10
 
3.1%
n 9
 
2.8%
s 7
 
2.1%
u 6
 
1.8%
C 6
 
1.8%
Other values (24) 55
 
16.9%
None
ValueCountFrequency (%)
ö 1
50.0%
ü 1
50.0%

unspez Fokus abgeklärt 0nein 1ja+neg 2ja+pos
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)2.6%
Missing1
Missing (%)0.5%
Memory size12.4 KiB
-
176 
1
 
8
0
 
5
2
 
1
Divertikel
 
1

Length

Max length10
Median length1
Mean length1.0471204
Min length1

Characters and Unicode

Total characters200
Distinct characters12
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.0%

Sample

1st row-
2nd row-
3rd row-
4th row-
5th row-

Common Values

ValueCountFrequency (%)
- 176
91.7%
1 8
 
4.2%
0 5
 
2.6%
2 1
 
0.5%
Divertikel 1
 
0.5%
(Missing) 1
 
0.5%

Length

2025-11-23T13:47:55.302536image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-23T13:47:55.378709image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
176
92.1%
1 8
 
4.2%
0 5
 
2.6%
2 1
 
0.5%
divertikel 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
- 176
88.0%
1 8
 
4.0%
0 5
 
2.5%
i 2
 
1.0%
e 2
 
1.0%
2 1
 
0.5%
D 1
 
0.5%
v 1
 
0.5%
r 1
 
0.5%
t 1
 
0.5%
Other values (2) 2
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Dash Punctuation 176
88.0%
Decimal Number 14
 
7.0%
Lowercase Letter 9
 
4.5%
Uppercase Letter 1
 
0.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 2
22.2%
e 2
22.2%
v 1
11.1%
r 1
11.1%
t 1
11.1%
k 1
11.1%
l 1
11.1%
Decimal Number
ValueCountFrequency (%)
1 8
57.1%
0 5
35.7%
2 1
 
7.1%
Dash Punctuation
ValueCountFrequency (%)
- 176
100.0%
Uppercase Letter
ValueCountFrequency (%)
D 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 190
95.0%
Latin 10
 
5.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 2
20.0%
e 2
20.0%
D 1
10.0%
v 1
10.0%
r 1
10.0%
t 1
10.0%
k 1
10.0%
l 1
10.0%
Common
ValueCountFrequency (%)
- 176
92.6%
1 8
 
4.2%
0 5
 
2.6%
2 1
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 200
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 176
88.0%
1 8
 
4.0%
0 5
 
2.5%
i 2
 
1.0%
e 2
 
1.0%
2 1
 
0.5%
D 1
 
0.5%
v 1
 
0.5%
r 1
 
0.5%
t 1
 
0.5%
Other values (2) 2
 
1.0%

weitere
Categorical

Imbalance  Missing 

Distinct13
Distinct (%)6.8%
Missing2
Missing (%)1.0%
Memory size12.5 KiB
-
178 
Ulcus, bekannt
 
1
BronchialCA
 
1
Zn Pankreatitis
 
1
Neurinom
 
1
Other values (8)
 
8

Length

Max length61
Median length1
Mean length1.9
Min length1

Characters and Unicode

Total characters361
Distinct characters39
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)6.3%

Sample

1st row-
2nd rowUlcus, bekannt
3rd rowBronchialCA
4th rowZn Pankreatitis
5th row-

Common Values

ValueCountFrequency (%)
- 178
92.7%
Ulcus, bekannt 1
 
0.5%
BronchialCA 1
 
0.5%
Zn Pankreatitis 1
 
0.5%
Neurinom 1
 
0.5%
Va ColonCA 1
 
0.5%
Mtx 1
 
0.5%
lymphogen pleural pulmonal metastasiertem Mammakarzinom links 1
 
0.5%
Struma 1
 
0.5%
NNR-RF, ae Adenom 1
 
0.5%
Other values (3) 3
 
1.6%
(Missing) 2
 
1.0%

Length

2025-11-23T13:47:55.483865image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
178
88.1%
adenom 2
 
1.0%
ulcus 1
 
0.5%
prostataca 1
 
0.5%
ed 1
 
0.5%
leberzirrhose 1
 
0.5%
ae 1
 
0.5%
nnr-rf 1
 
0.5%
struma 1
 
0.5%
links 1
 
0.5%
Other values (14) 14
 
6.9%

Most occurring characters

ValueCountFrequency (%)
- 179
49.6%
a 16
 
4.4%
e 14
 
3.9%
n 14
 
3.9%
o 13
 
3.6%
12
 
3.3%
m 11
 
3.0%
r 11
 
3.0%
t 10
 
2.8%
l 10
 
2.8%
Other values (29) 71
 
19.7%

Most occurring categories

ValueCountFrequency (%)
Dash Punctuation 179
49.6%
Lowercase Letter 140
38.8%
Uppercase Letter 28
 
7.8%
Space Separator 12
 
3.3%
Other Punctuation 2
 
0.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 16
11.4%
e 14
10.0%
n 14
10.0%
o 13
9.3%
m 11
7.9%
r 11
7.9%
t 10
 
7.1%
l 10
 
7.1%
i 8
 
5.7%
s 7
 
5.0%
Other values (11) 26
18.6%
Uppercase Letter
ValueCountFrequency (%)
A 5
17.9%
C 5
17.9%
N 3
10.7%
M 2
 
7.1%
R 2
 
7.1%
P 2
 
7.1%
L 1
 
3.6%
E 1
 
3.6%
F 1
 
3.6%
S 1
 
3.6%
Other values (5) 5
17.9%
Dash Punctuation
ValueCountFrequency (%)
- 179
100.0%
Space Separator
ValueCountFrequency (%)
12
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 193
53.5%
Latin 168
46.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 16
 
9.5%
e 14
 
8.3%
n 14
 
8.3%
o 13
 
7.7%
m 11
 
6.5%
r 11
 
6.5%
t 10
 
6.0%
l 10
 
6.0%
i 8
 
4.8%
s 7
 
4.2%
Other values (26) 54
32.1%
Common
ValueCountFrequency (%)
- 179
92.7%
12
 
6.2%
, 2
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 361
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 179
49.6%
a 16
 
4.4%
e 14
 
3.9%
n 14
 
3.9%
o 13
 
3.6%
12
 
3.3%
m 11
 
3.0%
r 11
 
3.0%
t 10
 
2.8%
l 10
 
2.8%
Other values (29) 71
 
19.7%

Thrombus
Categorical

Constant  Missing 

Distinct1
Distinct (%)6.7%
Missing177
Missing (%)92.2%
Memory size12.1 KiB
1.0
15 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters45
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 15
 
7.8%
(Missing) 177
92.2%

Length

2025-11-23T13:47:55.597070image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-23T13:47:55.653551image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 15
100.0%

Most occurring characters

ValueCountFrequency (%)
1 15
33.3%
. 15
33.3%
0 15
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 30
66.7%
Other Punctuation 15
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 15
50.0%
0 15
50.0%
Other Punctuation
ValueCountFrequency (%)
. 15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 45
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 15
33.3%
. 15
33.3%
0 15
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 15
33.3%
. 15
33.3%
0 15
33.3%

sex (1F, 2M)
Categorical

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size12.8 KiB
2.0
126 
1.0
66 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters576
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row2.0
3rd row1.0
4th row1.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 126
65.6%
1.0 66
34.4%

Length

2025-11-23T13:47:55.719706image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-23T13:47:55.777247image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2.0 126
65.6%
1.0 66
34.4%

Most occurring characters

ValueCountFrequency (%)
. 192
33.3%
0 192
33.3%
2 126
21.9%
1 66
 
11.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 384
66.7%
Other Punctuation 192
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 192
50.0%
2 126
32.8%
1 66
 
17.2%
Other Punctuation
ValueCountFrequency (%)
. 192
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 576
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 192
33.3%
0 192
33.3%
2 126
21.9%
1 66
 
11.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 576
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 192
33.3%
0 192
33.3%
2 126
21.9%
1 66
 
11.5%

Diagnose
Text

Distinct179
Distinct (%)93.2%
Missing0
Missing (%)0.0%
Memory size22.5 KiB
2025-11-23T13:47:55.896109image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length216
Median length89.5
Mean length46.286458
Min length4

Characters and Unicode

Total characters8887
Distinct characters71
Distinct categories10 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique170 ?
Unique (%)88.5%

Sample

1st rowSpondylodiszitis TH5/6
2nd rowDiszitis L4/5
3rd rowSpondylodiszitis L4/5/S1+Empyem
4th rowAusschluss Diszitis
5th rowWHST
ValueCountFrequency (%)
spondylodiszitis 116
 
10.1%
bei 37
 
3.2%
z.n 36
 
3.1%
zn 34
 
3.0%
29
 
2.5%
empyem 28
 
2.4%
l4/5 27
 
2.3%
diszitis 27
 
2.3%
mit 26
 
2.3%
und 25
 
2.2%
Other values (373) 766
66.6%
2025-11-23T13:47:56.184443image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
961
 
10.8%
i 692
 
7.8%
s 600
 
6.8%
n 430
 
4.8%
o 425
 
4.8%
e 411
 
4.6%
d 347
 
3.9%
t 322
 
3.6%
l 270
 
3.0%
S 243
 
2.7%
Other values (61) 4186
47.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5249
59.1%
Uppercase Letter 1269
 
14.3%
Space Separator 961
 
10.8%
Decimal Number 801
 
9.0%
Other Punctuation 451
 
5.1%
Dash Punctuation 98
 
1.1%
Math Symbol 52
 
0.6%
Open Punctuation 2
 
< 0.1%
Close Punctuation 2
 
< 0.1%
Control 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 692
13.2%
s 600
11.4%
n 430
 
8.2%
o 425
 
8.1%
e 411
 
7.8%
d 347
 
6.6%
t 322
 
6.1%
l 270
 
5.1%
p 234
 
4.5%
r 213
 
4.1%
Other values (18) 1305
24.9%
Uppercase Letter
ValueCountFrequency (%)
S 243
19.1%
L 225
17.7%
W 197
15.5%
Z 85
 
6.7%
K 66
 
5.2%
B 59
 
4.6%
D 55
 
4.3%
E 49
 
3.9%
H 43
 
3.4%
A 43
 
3.4%
Other values (13) 204
16.1%
Decimal Number
ValueCountFrequency (%)
1 173
21.6%
5 128
16.0%
4 123
15.4%
2 121
15.1%
3 85
10.6%
0 48
 
6.0%
6 44
 
5.5%
7 36
 
4.5%
9 24
 
3.0%
8 19
 
2.4%
Other Punctuation
ValueCountFrequency (%)
/ 234
51.9%
. 125
27.7%
, 90
 
20.0%
# 2
 
0.4%
Space Separator
ValueCountFrequency (%)
961
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 98
100.0%
Math Symbol
ValueCountFrequency (%)
+ 52
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Control
ValueCountFrequency (%)
 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6518
73.3%
Common 2369
 
26.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 692
 
10.6%
s 600
 
9.2%
n 430
 
6.6%
o 425
 
6.5%
e 411
 
6.3%
d 347
 
5.3%
t 322
 
4.9%
l 270
 
4.1%
S 243
 
3.7%
p 234
 
3.6%
Other values (41) 2544
39.0%
Common
ValueCountFrequency (%)
961
40.6%
/ 234
 
9.9%
1 173
 
7.3%
5 128
 
5.4%
. 125
 
5.3%
4 123
 
5.2%
2 121
 
5.1%
- 98
 
4.1%
, 90
 
3.8%
3 85
 
3.6%
Other values (10) 231
 
9.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8874
99.9%
None 13
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
961
 
10.8%
i 692
 
7.8%
s 600
 
6.8%
n 430
 
4.8%
o 425
 
4.8%
e 411
 
4.6%
d 347
 
3.9%
t 322
 
3.6%
l 270
 
3.0%
S 243
 
2.7%
Other values (58) 4173
47.0%
None
ValueCountFrequency (%)
ä 9
69.2%
ö 3
 
23.1%
ü 1
 
7.7%

LWS
Categorical

Distinct2
Distinct (%)1.0%
Missing1
Missing (%)0.5%
Memory size12.7 KiB
1.0
133 
0.0
58 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters573
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 133
69.3%
0.0 58
30.2%
(Missing) 1
 
0.5%

Length

2025-11-23T13:47:56.466869image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-23T13:47:56.527830image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 133
69.6%
0.0 58
30.4%

Most occurring characters

ValueCountFrequency (%)
0 249
43.5%
. 191
33.3%
1 133
23.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 382
66.7%
Other Punctuation 191
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 249
65.2%
1 133
34.8%
Other Punctuation
ValueCountFrequency (%)
. 191
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 573
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 249
43.5%
. 191
33.3%
1 133
23.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 573
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 249
43.5%
. 191
33.3%
1 133
23.2%

BWS
Categorical

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size12.8 KiB
0.0
135 
1.0
57 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters576
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 135
70.3%
1.0 57
29.7%

Length

2025-11-23T13:47:56.603585image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-23T13:47:56.661695image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 135
70.3%
1.0 57
29.7%

Most occurring characters

ValueCountFrequency (%)
0 327
56.8%
. 192
33.3%
1 57
 
9.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 384
66.7%
Other Punctuation 192
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 327
85.2%
1 57
 
14.8%
Other Punctuation
ValueCountFrequency (%)
. 192
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 576
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 327
56.8%
. 192
33.3%
1 57
 
9.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 576
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 327
56.8%
. 192
33.3%
1 57
 
9.9%

HWS
Categorical

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size12.8 KiB
0.0
157 
1.0
35 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters576
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 157
81.8%
1.0 35
 
18.2%

Length

2025-11-23T13:47:56.733598image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-23T13:47:56.790712image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 157
81.8%
1.0 35
 
18.2%

Most occurring characters

ValueCountFrequency (%)
0 349
60.6%
. 192
33.3%
1 35
 
6.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 384
66.7%
Other Punctuation 192
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 349
90.9%
1 35
 
9.1%
Other Punctuation
ValueCountFrequency (%)
. 192
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 576
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 349
60.6%
. 192
33.3%
1 35
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 576
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 349
60.6%
. 192
33.3%
1 35
 
6.1%

intraspinal
Categorical

Distinct3
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size12.8 KiB
0.0
136 
1.0
53 
2.0
 
3

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters576
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 136
70.8%
1.0 53
 
27.6%
2.0 3
 
1.6%

Length

2025-11-23T13:47:56.862528image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-23T13:47:56.924511image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 136
70.8%
1.0 53
 
27.6%
2.0 3
 
1.6%

Most occurring characters

ValueCountFrequency (%)
0 328
56.9%
. 192
33.3%
1 53
 
9.2%
2 3
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 384
66.7%
Other Punctuation 192
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 328
85.4%
1 53
 
13.8%
2 3
 
0.8%
Other Punctuation
ValueCountFrequency (%)
. 192
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 576
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 328
56.9%
. 192
33.3%
1 53
 
9.2%
2 3
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 576
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 328
56.9%
. 192
33.3%
1 53
 
9.2%
2 3
 
0.5%

biopsy
Categorical

Imbalance 

Distinct4
Distinct (%)2.1%
Missing1
Missing (%)0.5%
Memory size12.4 KiB
0
130 
1
59 
1 (LWS)
 
1
2
 
1

Length

Max length7
Median length1
Mean length1.0314136
Min length1

Characters and Unicode

Total characters197
Distinct characters9
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 130
67.7%
1 59
30.7%
1 (LWS) 1
 
0.5%
2 1
 
0.5%
(Missing) 1
 
0.5%

Length

2025-11-23T13:47:57.011538image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-23T13:47:57.085967image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 130
67.7%
1 60
31.2%
lws 1
 
0.5%
2 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 130
66.0%
1 60
30.5%
1
 
0.5%
( 1
 
0.5%
L 1
 
0.5%
W 1
 
0.5%
S 1
 
0.5%
) 1
 
0.5%
2 1
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 191
97.0%
Uppercase Letter 3
 
1.5%
Space Separator 1
 
0.5%
Open Punctuation 1
 
0.5%
Close Punctuation 1
 
0.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 130
68.1%
1 60
31.4%
2 1
 
0.5%
Uppercase Letter
ValueCountFrequency (%)
L 1
33.3%
W 1
33.3%
S 1
33.3%
Space Separator
ValueCountFrequency (%)
1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 194
98.5%
Latin 3
 
1.5%

Most frequent character per script

Common
ValueCountFrequency (%)
0 130
67.0%
1 60
30.9%
1
 
0.5%
( 1
 
0.5%
) 1
 
0.5%
2 1
 
0.5%
Latin
ValueCountFrequency (%)
L 1
33.3%
W 1
33.3%
S 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 197
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 130
66.0%
1 60
30.5%
1
 
0.5%
( 1
 
0.5%
L 1
 
0.5%
W 1
 
0.5%
S 1
 
0.5%
) 1
 
0.5%
2 1
 
0.5%

OP
Text

Missing 

Distinct163
Distinct (%)86.2%
Missing3
Missing (%)1.6%
Memory size25.1 KiB
2025-11-23T13:47:57.209306image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length614
Median length86
Mean length45.888889
Min length1

Characters and Unicode

Total characters8673
Distinct characters73
Distinct categories10 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique157 ?
Unique (%)83.1%

Sample

1st rowSolera TH4-5-6-7 + Deko+BSF-Ausräumung
2nd rowSolera LW2-3-S2-IA, ALIF L4/5
3rd rowSolera L4-5-S1+Deko, ALIF L4/5, 5/S1
4th row-
5th rowVerlängerung L2-3-4-5-S1-S2IA
ValueCountFrequency (%)
solera 100
 
8.4%
59
 
4.9%
xlif 35
 
2.9%
wke 23
 
1.9%
l4/5 18
 
1.5%
acdf 17
 
1.4%
deko 17
 
1.4%
von 17
 
1.4%
links 17
 
1.4%
und 16
 
1.3%
Other values (465) 875
73.3%
2025-11-23T13:47:57.507450image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1007
 
11.6%
e 612
 
7.1%
- 384
 
4.4%
n 334
 
3.9%
L 318
 
3.7%
r 312
 
3.6%
o 308
 
3.6%
a 301
 
3.5%
i 293
 
3.4%
l 259
 
3.0%
Other values (63) 4545
52.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3982
45.9%
Uppercase Letter 1741
20.1%
Space Separator 1007
 
11.6%
Decimal Number 997
 
11.5%
Other Punctuation 426
 
4.9%
Dash Punctuation 384
 
4.4%
Math Symbol 93
 
1.1%
Close Punctuation 21
 
0.2%
Open Punctuation 21
 
0.2%
Control 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 612
15.4%
n 334
 
8.4%
r 312
 
7.8%
o 308
 
7.7%
a 301
 
7.6%
i 293
 
7.4%
l 259
 
6.5%
s 247
 
6.2%
t 238
 
6.0%
u 170
 
4.3%
Other values (18) 908
22.8%
Uppercase Letter
ValueCountFrequency (%)
L 318
18.3%
S 243
14.0%
W 215
12.3%
F 115
 
6.6%
I 97
 
5.6%
A 86
 
4.9%
D 86
 
4.9%
K 84
 
4.8%
B 79
 
4.5%
E 76
 
4.4%
Other values (15) 342
19.6%
Decimal Number
ValueCountFrequency (%)
1 239
24.0%
4 160
16.0%
2 160
16.0%
5 157
15.7%
3 128
12.8%
6 44
 
4.4%
7 38
 
3.8%
0 25
 
2.5%
9 24
 
2.4%
8 22
 
2.2%
Other Punctuation
ValueCountFrequency (%)
, 207
48.6%
/ 173
40.6%
. 44
 
10.3%
: 2
 
0.5%
Space Separator
ValueCountFrequency (%)
1007
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 384
100.0%
Math Symbol
ValueCountFrequency (%)
+ 93
100.0%
Close Punctuation
ValueCountFrequency (%)
) 21
100.0%
Open Punctuation
ValueCountFrequency (%)
( 21
100.0%
Control
ValueCountFrequency (%)
 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5723
66.0%
Common 2950
34.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 612
 
10.7%
n 334
 
5.8%
L 318
 
5.6%
r 312
 
5.5%
o 308
 
5.4%
a 301
 
5.3%
i 293
 
5.1%
l 259
 
4.5%
s 247
 
4.3%
S 243
 
4.2%
Other values (43) 2496
43.6%
Common
ValueCountFrequency (%)
1007
34.1%
- 384
 
13.0%
1 239
 
8.1%
, 207
 
7.0%
/ 173
 
5.9%
4 160
 
5.4%
2 160
 
5.4%
5 157
 
5.3%
3 128
 
4.3%
+ 93
 
3.2%
Other values (10) 242
 
8.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8626
99.5%
None 47
 
0.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1007
 
11.7%
e 612
 
7.1%
- 384
 
4.5%
n 334
 
3.9%
L 318
 
3.7%
r 312
 
3.6%
o 308
 
3.6%
a 301
 
3.5%
i 293
 
3.4%
l 259
 
3.0%
Other values (58) 4498
52.1%
None
ValueCountFrequency (%)
ä 28
59.6%
ö 14
29.8%
ü 3
 
6.4%
é 1
 
2.1%
Ö 1
 
2.1%

DOA
Date

Distinct183
Distinct (%)95.3%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
Minimum2016-03-21 00:00:00
Maximum2023-12-31 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-11-23T13:47:57.628240image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T13:47:57.767128image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

surgery date
Text

Missing 

Distinct162
Distinct (%)92.0%
Missing16
Missing (%)8.3%
Memory size14.3 KiB
2025-11-23T13:47:57.949683image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length87
Median length10
Mean length14.653409
Min length1

Characters and Unicode

Total characters2579
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique155 ?
Unique (%)88.1%

Sample

1st row30.05.2022
2nd row03.03.2022
3rd row22.12.2022
4th row-
5th row01.08.2023
ValueCountFrequency (%)
8
 
3.1%
24.10.2023 3
 
1.2%
06.08.2021 3
 
1.2%
26.11.2020 2
 
0.8%
06.05.2020 2
 
0.8%
05.05.2020 2
 
0.8%
17.03.2020 2
 
0.8%
31.03.2020 2
 
0.8%
13.07.2020 2
 
0.8%
10.09.2020 2
 
0.8%
Other values (220) 229
89.1%
2025-11-23T13:47:58.237032image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 663
25.7%
0 640
24.8%
. 498
19.3%
1 274
10.6%
3 97
 
3.8%
81
 
3.1%
8 63
 
2.4%
9 63
 
2.4%
6 52
 
2.0%
7 48
 
1.9%
Other values (3) 100
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1992
77.2%
Other Punctuation 498
 
19.3%
Space Separator 81
 
3.1%
Dash Punctuation 8
 
0.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 663
33.3%
0 640
32.1%
1 274
13.8%
3 97
 
4.9%
8 63
 
3.2%
9 63
 
3.2%
6 52
 
2.6%
7 48
 
2.4%
4 46
 
2.3%
5 46
 
2.3%
Other Punctuation
ValueCountFrequency (%)
. 498
100.0%
Space Separator
ValueCountFrequency (%)
81
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2579
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 663
25.7%
0 640
24.8%
. 498
19.3%
1 274
10.6%
3 97
 
3.8%
81
 
3.1%
8 63
 
2.4%
9 63
 
2.4%
6 52
 
2.0%
7 48
 
1.9%
Other values (3) 100
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2579
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 663
25.7%
0 640
24.8%
. 498
19.3%
1 274
10.6%
3 97
 
3.8%
81
 
3.1%
8 63
 
2.4%
9 63
 
2.4%
6 52
 
2.0%
7 48
 
1.9%
Other values (3) 100
 
3.9%

date of PET
Date

Missing 

Distinct170
Distinct (%)91.4%
Missing6
Missing (%)3.1%
Memory size3.0 KiB
Minimum2016-03-23 00:00:00
Maximum2024-02-20 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-11-23T13:47:58.366042image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T13:47:58.524997image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

mibi surgery 0 = neg
Text

Distinct54
Distinct (%)28.1%
Missing0
Missing (%)0.0%
Memory size15.3 KiB
2025-11-23T13:47:58.707371image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length94
Median length75
Mean length16.671875
Min length1

Characters and Unicode

Total characters3201
Distinct characters46
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique41 ?
Unique (%)21.4%

Sample

1st rowStreptococcus dysgalactiae, Staphylococcus capitis
2nd rowStaphylococcus epidermidis, Staphylococcus warneri
3rd rowStaphylococcus epidermidis
4th rownot done
5th rowStaphylococcus epidermidis
ValueCountFrequency (%)
0 49
12.8%
stau 41
 
10.7%
staphylococcus 39
 
10.2%
acnes 25
 
6.5%
cutibacterium 25
 
6.5%
epidermidis 22
 
5.7%
propionibacterium 22
 
5.7%
done 18
 
4.7%
not 18
 
4.7%
e 14
 
3.7%
Other values (62) 110
28.7%
2025-11-23T13:47:59.011704image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
c 280
 
8.7%
i 262
 
8.2%
o 228
 
7.1%
e 222
 
6.9%
a 216
 
6.7%
191
 
6.0%
t 183
 
5.7%
u 158
 
4.9%
r 157
 
4.9%
s 145
 
4.5%
Other values (36) 1159
36.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2545
79.5%
Uppercase Letter 317
 
9.9%
Space Separator 191
 
6.0%
Other Punctuation 52
 
1.6%
Decimal Number 49
 
1.5%
Open Punctuation 23
 
0.7%
Close Punctuation 23
 
0.7%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
c 280
11.0%
i 262
10.3%
o 228
9.0%
e 222
 
8.7%
a 216
 
8.5%
t 183
 
7.2%
u 158
 
6.2%
r 157
 
6.2%
s 145
 
5.7%
n 133
 
5.2%
Other values (13) 561
22.0%
Uppercase Letter
ValueCountFrequency (%)
S 96
30.3%
A 46
14.5%
U 41
12.9%
T 41
12.9%
P 28
 
8.8%
C 27
 
8.5%
E 20
 
6.3%
G 4
 
1.3%
B 4
 
1.3%
M 3
 
0.9%
Other values (5) 7
 
2.2%
Other Punctuation
ValueCountFrequency (%)
, 32
61.5%
. 19
36.5%
: 1
 
1.9%
Space Separator
ValueCountFrequency (%)
191
100.0%
Decimal Number
ValueCountFrequency (%)
0 49
100.0%
Open Punctuation
ValueCountFrequency (%)
( 23
100.0%
Close Punctuation
ValueCountFrequency (%)
) 23
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2862
89.4%
Common 339
 
10.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
c 280
 
9.8%
i 262
 
9.2%
o 228
 
8.0%
e 222
 
7.8%
a 216
 
7.5%
t 183
 
6.4%
u 158
 
5.5%
r 157
 
5.5%
s 145
 
5.1%
n 133
 
4.6%
Other values (28) 878
30.7%
Common
ValueCountFrequency (%)
191
56.3%
0 49
 
14.5%
, 32
 
9.4%
( 23
 
6.8%
) 23
 
6.8%
. 19
 
5.6%
: 1
 
0.3%
- 1
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3201
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
c 280
 
8.7%
i 262
 
8.2%
o 228
 
7.1%
e 222
 
6.9%
a 216
 
6.7%
191
 
6.0%
t 183
 
5.7%
u 158
 
4.9%
r 157
 
4.9%
s 145
 
4.5%
Other values (36) 1159
36.2%

1 = lowgrade 2 = highgrade
Categorical

High correlation  Missing 

Distinct5
Distinct (%)3.6%
Missing54
Missing (%)28.1%
Memory size12.3 KiB
2
82 
1
38 
0
16 
xx
 
1
3
 
1

Length

Max length2
Median length1
Mean length1.0072464
Min length1

Characters and Unicode

Total characters139
Distinct characters5
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.4%

Sample

1st row2
2nd row1
3rd row1
4th row1
5th row2

Common Values

ValueCountFrequency (%)
2 82
42.7%
1 38
19.8%
0 16
 
8.3%
xx 1
 
0.5%
3 1
 
0.5%
(Missing) 54
28.1%

Length

2025-11-23T13:47:59.130014image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-23T13:47:59.246027image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2 82
59.4%
1 38
27.5%
0 16
 
11.6%
xx 1
 
0.7%
3 1
 
0.7%

Most occurring characters

ValueCountFrequency (%)
2 82
59.0%
1 38
27.3%
0 16
 
11.5%
x 2
 
1.4%
3 1
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 137
98.6%
Lowercase Letter 2
 
1.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 82
59.9%
1 38
27.7%
0 16
 
11.7%
3 1
 
0.7%
Lowercase Letter
ValueCountFrequency (%)
x 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 137
98.6%
Latin 2
 
1.4%

Most frequent character per script

Common
ValueCountFrequency (%)
2 82
59.9%
1 38
27.7%
0 16
 
11.7%
3 1
 
0.7%
Latin
ValueCountFrequency (%)
x 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 139
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 82
59.0%
1 38
27.3%
0 16
 
11.5%
x 2
 
1.4%
3 1
 
0.7%

histo surgery 3 intermediär 0 neg
Categorical

Imbalance 

Distinct8
Distinct (%)4.2%
Missing1
Missing (%)0.5%
Memory size12.7 KiB
1
135 
not done
28 
0
19 
2
 
5
Author: intermediär (chron granulierend) 3
 
1
Other values (3)
 
3

Length

Max length42
Median length1
Mean length2.408377
Min length1

Characters and Unicode

Total characters460
Distinct characters31
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)2.1%

Sample

1st row1
2nd row1
3rd row1
4th rownot done
5th row1

Common Values

ValueCountFrequency (%)
1 135
70.3%
not done 28
 
14.6%
0 19
 
9.9%
2 5
 
2.6%
Author: intermediär (chron granulierend) 3 1
 
0.5%
1 B-Zell Lymphom 1
 
0.5%
3 1
 
0.5%
Author: unsicher 3 1
 
0.5%
(Missing) 1
 
0.5%

Length

2025-11-23T13:47:59.384651image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-23T13:47:59.493722image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 136
59.9%
not 28
 
12.3%
done 28
 
12.3%
0 19
 
8.4%
2 5
 
2.2%
3 3
 
1.3%
author 2
 
0.9%
intermediär 1
 
0.4%
chron 1
 
0.4%
granulierend 1
 
0.4%
Other values (3) 3
 
1.3%

Most occurring characters

ValueCountFrequency (%)
1 136
29.6%
n 61
13.3%
o 60
13.0%
36
 
7.8%
e 34
 
7.4%
t 31
 
6.7%
d 30
 
6.5%
0 19
 
4.1%
r 8
 
1.7%
2 5
 
1.1%
Other values (21) 40
 
8.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 251
54.6%
Decimal Number 163
35.4%
Space Separator 36
 
7.8%
Uppercase Letter 5
 
1.1%
Other Punctuation 2
 
0.4%
Open Punctuation 1
 
0.2%
Close Punctuation 1
 
0.2%
Dash Punctuation 1
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 61
24.3%
o 60
23.9%
e 34
13.5%
t 31
12.4%
d 30
12.0%
r 8
 
3.2%
h 5
 
2.0%
u 4
 
1.6%
i 4
 
1.6%
l 3
 
1.2%
Other values (8) 11
 
4.4%
Decimal Number
ValueCountFrequency (%)
1 136
83.4%
0 19
 
11.7%
2 5
 
3.1%
3 3
 
1.8%
Uppercase Letter
ValueCountFrequency (%)
A 2
40.0%
B 1
20.0%
Z 1
20.0%
L 1
20.0%
Space Separator
ValueCountFrequency (%)
36
100.0%
Other Punctuation
ValueCountFrequency (%)
: 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 256
55.7%
Common 204
44.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 61
23.8%
o 60
23.4%
e 34
13.3%
t 31
12.1%
d 30
11.7%
r 8
 
3.1%
h 5
 
2.0%
u 4
 
1.6%
i 4
 
1.6%
l 3
 
1.2%
Other values (12) 16
 
6.2%
Common
ValueCountFrequency (%)
1 136
66.7%
36
 
17.6%
0 19
 
9.3%
2 5
 
2.5%
3 3
 
1.5%
: 2
 
1.0%
( 1
 
0.5%
) 1
 
0.5%
- 1
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 459
99.8%
None 1
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 136
29.6%
n 61
13.3%
o 60
13.1%
36
 
7.8%
e 34
 
7.4%
t 31
 
6.8%
d 30
 
6.5%
0 19
 
4.1%
r 8
 
1.7%
2 5
 
1.1%
Other values (20) 39
 
8.5%
None
ValueCountFrequency (%)
ä 1
100.0%

mibi other
Text

Missing 

Distinct51
Distinct (%)28.2%
Missing11
Missing (%)5.7%
Memory size13.9 KiB
2025-11-23T13:47:59.674172image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length77
Median length1
Mean length10.370166
Min length1

Characters and Unicode

Total characters1877
Distinct characters49
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique43 ?
Unique (%)23.8%

Sample

1st row-
2nd row-
3rd row-
4th row-
5th row-
ValueCountFrequency (%)
85
25.0%
bk 66
19.4%
stau 42
12.4%
0 11
 
3.2%
staphylococcus 11
 
3.2%
e 8
 
2.4%
coli 8
 
2.4%
streptococcus 6
 
1.8%
knie 5
 
1.5%
epidermidis 5
 
1.5%
Other values (60) 93
27.4%
2025-11-23T13:47:59.965180image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
159
 
8.5%
c 110
 
5.9%
e 100
 
5.3%
a 100
 
5.3%
- 86
 
4.6%
) 86
 
4.6%
i 85
 
4.5%
( 84
 
4.5%
o 84
 
4.5%
S 78
 
4.2%
Other values (39) 905
48.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1018
54.2%
Uppercase Letter 403
 
21.5%
Space Separator 159
 
8.5%
Dash Punctuation 86
 
4.6%
Close Punctuation 86
 
4.6%
Open Punctuation 84
 
4.5%
Other Punctuation 30
 
1.6%
Decimal Number 11
 
0.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
c 110
10.8%
e 100
9.8%
a 100
9.8%
i 85
 
8.3%
o 84
 
8.3%
s 75
 
7.4%
t 73
 
7.2%
r 61
 
6.0%
u 56
 
5.5%
l 49
 
4.8%
Other values (16) 225
22.1%
Uppercase Letter
ValueCountFrequency (%)
S 78
19.4%
K 71
17.6%
B 70
17.4%
A 51
12.7%
U 45
11.2%
T 43
10.7%
E 16
 
4.0%
P 7
 
1.7%
H 5
 
1.2%
M 4
 
1.0%
Other values (6) 13
 
3.2%
Other Punctuation
ValueCountFrequency (%)
, 17
56.7%
. 13
43.3%
Space Separator
ValueCountFrequency (%)
159
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 86
100.0%
Close Punctuation
ValueCountFrequency (%)
) 86
100.0%
Open Punctuation
ValueCountFrequency (%)
( 84
100.0%
Decimal Number
ValueCountFrequency (%)
0 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1421
75.7%
Common 456
 
24.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
c 110
 
7.7%
e 100
 
7.0%
a 100
 
7.0%
i 85
 
6.0%
o 84
 
5.9%
S 78
 
5.5%
s 75
 
5.3%
t 73
 
5.1%
K 71
 
5.0%
B 70
 
4.9%
Other values (32) 575
40.5%
Common
ValueCountFrequency (%)
159
34.9%
- 86
18.9%
) 86
18.9%
( 84
18.4%
, 17
 
3.7%
. 13
 
2.9%
0 11
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1873
99.8%
None 4
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
159
 
8.5%
c 110
 
5.9%
e 100
 
5.3%
a 100
 
5.3%
- 86
 
4.6%
) 86
 
4.6%
i 85
 
4.5%
( 84
 
4.5%
o 84
 
4.5%
S 78
 
4.2%
Other values (36) 901
48.1%
None
ValueCountFrequency (%)
ü 2
50.0%
ä 1
25.0%
ß 1
25.0%

CRP initial
Text

Distinct128
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Memory size12.8 KiB
2025-11-23T13:48:00.189092image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.2135417
Min length1

Characters and Unicode

Total characters617
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique84 ?
Unique (%)43.8%

Sample

1st row3,6
2nd row11,2
3rd row2,9
4th row12
5th row13,7
ValueCountFrequency (%)
0,3 8
 
4.2%
4,6 5
 
2.6%
0,5 4
 
2.1%
11,2 3
 
1.6%
4,1 3
 
1.6%
2,8 3
 
1.6%
0,8 3
 
1.6%
0,1 3
 
1.6%
1,5 3
 
1.6%
9,1 3
 
1.6%
Other values (118) 154
80.2%
2025-11-23T13:48:00.509681image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 178
28.8%
1 87
14.1%
2 69
 
11.2%
4 43
 
7.0%
3 42
 
6.8%
6 38
 
6.2%
9 34
 
5.5%
0 32
 
5.2%
7 32
 
5.2%
5 31
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 439
71.2%
Other Punctuation 178
28.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 87
19.8%
2 69
15.7%
4 43
9.8%
3 42
9.6%
6 38
8.7%
9 34
 
7.7%
0 32
 
7.3%
7 32
 
7.3%
5 31
 
7.1%
8 31
 
7.1%
Other Punctuation
ValueCountFrequency (%)
, 178
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 617
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 178
28.8%
1 87
14.1%
2 69
 
11.2%
4 43
 
7.0%
3 42
 
6.8%
6 38
 
6.2%
9 34
 
5.5%
0 32
 
5.2%
7 32
 
5.2%
5 31
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 617
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 178
28.8%
1 87
14.1%
2 69
 
11.2%
4 43
 
7.0%
3 42
 
6.8%
6 38
 
6.2%
9 34
 
5.5%
0 32
 
5.2%
7 32
 
5.2%
5 31
 
5.0%

PETCT TE
Unsupported

Missing  Rejected  Unsupported 

Missing192
Missing (%)100.0%
Memory size3.0 KiB

TE (at all)
Categorical

Imbalance 

Distinct3
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size12.8 KiB
1.0
163 
0.0
25 
2.0
 
4

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters576
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 163
84.9%
0.0 25
 
13.0%
2.0 4
 
2.1%

Length

2025-11-23T13:48:00.615045image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-23T13:48:00.679528image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 163
84.9%
0.0 25
 
13.0%
2.0 4
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 217
37.7%
. 192
33.3%
1 163
28.3%
2 4
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 384
66.7%
Other Punctuation 192
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 217
56.5%
1 163
42.4%
2 4
 
1.0%
Other Punctuation
ValueCountFrequency (%)
. 192
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 576
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 217
37.7%
. 192
33.3%
1 163
28.3%
2 4
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 576
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 217
37.7%
. 192
33.3%
1 163
28.3%
2 4
 
0.7%

discitis in MRT = TE, 2 Frage diszitis b MRT unklar, 0 = n übereinstimmend, 3 kein MRT, 4 Ausschluss Diszitis, 5 Diszitis im MRT n erkannt, 6 neuer Nachweis Diszitis
Categorical

High correlation  Imbalance 

Distinct17
Distinct (%)8.9%
Missing0
Missing (%)0.0%
Memory size12.8 KiB
1
129 
2
28 
0
 
11
3
 
7
4
 
4
Other values (12)
13 

Length

Max length69
Median length1
Mean length2.65625
Min length1

Characters and Unicode

Total characters510
Distinct characters44
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)5.7%

Sample

1st row1
2nd row1
3rd row1
4th row2
5th row1

Common Values

ValueCountFrequency (%)
1 129
67.2%
2 28
 
14.6%
0 11
 
5.7%
3 7
 
3.6%
4 4
 
2.1%
2, 4 2
 
1.0%
Author: kein MRT 3, 4 1
 
0.5%
Author: kein MRT vorhanden 3 1
 
0.5%
Author: in MRT keine Diszitis, Fraktur drüber als Diszitis gewertet 2 1
 
0.5%
Author: vorzeitiger Abbruch 3 1
 
0.5%
Other values (7) 7
 
3.6%

Length

2025-11-23T13:48:00.780561image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1 131
54.4%
2 32
 
13.3%
3 12
 
5.0%
0 11
 
4.6%
author 9
 
3.7%
4 7
 
2.9%
mrt 6
 
2.5%
kein 4
 
1.7%
diszitis 3
 
1.2%
gewertet 2
 
0.8%
Other values (23) 24
 
10.0%

Most occurring characters

ValueCountFrequency (%)
1 131
25.7%
49
 
9.6%
2 32
 
6.3%
i 26
 
5.1%
e 23
 
4.5%
t 23
 
4.5%
r 21
 
4.1%
u 15
 
2.9%
n 15
 
2.9%
h 14
 
2.7%
Other values (34) 161
31.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 210
41.2%
Decimal Number 195
38.2%
Space Separator 49
 
9.6%
Uppercase Letter 41
 
8.0%
Other Punctuation 13
 
2.5%
Open Punctuation 1
 
0.2%
Close Punctuation 1
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 26
12.4%
e 23
11.0%
t 23
11.0%
r 21
10.0%
u 15
 
7.1%
n 15
 
7.1%
h 14
 
6.7%
o 13
 
6.2%
s 11
 
5.2%
g 7
 
3.3%
Other values (11) 42
20.0%
Uppercase Letter
ValueCountFrequency (%)
A 10
24.4%
T 7
17.1%
M 7
17.1%
R 6
14.6%
D 4
 
9.8%
F 2
 
4.9%
Z 1
 
2.4%
B 1
 
2.4%
S 1
 
2.4%
P 1
 
2.4%
Decimal Number
ValueCountFrequency (%)
1 131
67.2%
2 32
 
16.4%
3 12
 
6.2%
0 11
 
5.6%
4 7
 
3.6%
5 1
 
0.5%
6 1
 
0.5%
Other Punctuation
ValueCountFrequency (%)
: 9
69.2%
, 4
30.8%
Space Separator
ValueCountFrequency (%)
49
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 259
50.8%
Latin 251
49.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 26
 
10.4%
e 23
 
9.2%
t 23
 
9.2%
r 21
 
8.4%
u 15
 
6.0%
n 15
 
6.0%
h 14
 
5.6%
o 13
 
5.2%
s 11
 
4.4%
A 10
 
4.0%
Other values (22) 80
31.9%
Common
ValueCountFrequency (%)
1 131
50.6%
49
 
18.9%
2 32
 
12.4%
3 12
 
4.6%
0 11
 
4.2%
: 9
 
3.5%
4 7
 
2.7%
, 4
 
1.5%
5 1
 
0.4%
( 1
 
0.4%
Other values (2) 2
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 509
99.8%
None 1
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 131
25.7%
49
 
9.6%
2 32
 
6.3%
i 26
 
5.1%
e 23
 
4.5%
t 23
 
4.5%
r 21
 
4.1%
u 15
 
2.9%
n 15
 
2.9%
h 14
 
2.8%
Other values (33) 160
31.4%
None
ValueCountFrequency (%)
ü 1
100.0%

other spinal TE
Categorical

Imbalance 

Distinct4
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size12.6 KiB
0
183 
1
 
6
3
 
2
Author: Empyem zusätzlich, zweite OP dadurch indiziert (stagnierende Infektwerte) 1
 
1

Length

Max length83
Median length1
Mean length1.4270833
Min length1

Characters and Unicode

Total characters274
Distinct characters35
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 183
95.3%
1 6
 
3.1%
3 2
 
1.0%
Author: Empyem zusätzlich, zweite OP dadurch indiziert (stagnierende Infektwerte) 1 1
 
0.5%

Length

2025-11-23T13:48:00.883720image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-23T13:48:00.958770image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 183
91.0%
1 7
 
3.5%
3 2
 
1.0%
author 1
 
0.5%
empyem 1
 
0.5%
zusätzlich 1
 
0.5%
zweite 1
 
0.5%
op 1
 
0.5%
dadurch 1
 
0.5%
indiziert 1
 
0.5%
Other values (2) 2
 
1.0%

Most occurring characters

ValueCountFrequency (%)
0 183
66.8%
e 10
 
3.6%
9
 
3.3%
t 7
 
2.6%
1 7
 
2.6%
i 6
 
2.2%
r 5
 
1.8%
n 4
 
1.5%
d 4
 
1.5%
z 4
 
1.5%
Other values (25) 35
 
12.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 192
70.1%
Lowercase Letter 64
 
23.4%
Space Separator 9
 
3.3%
Uppercase Letter 5
 
1.8%
Other Punctuation 2
 
0.7%
Open Punctuation 1
 
0.4%
Close Punctuation 1
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 10
15.6%
t 7
10.9%
i 6
 
9.4%
r 5
 
7.8%
n 4
 
6.2%
d 4
 
6.2%
z 4
 
6.2%
u 3
 
4.7%
h 3
 
4.7%
a 2
 
3.1%
Other values (12) 16
25.0%
Uppercase Letter
ValueCountFrequency (%)
I 1
20.0%
A 1
20.0%
P 1
20.0%
O 1
20.0%
E 1
20.0%
Decimal Number
ValueCountFrequency (%)
0 183
95.3%
1 7
 
3.6%
3 2
 
1.0%
Other Punctuation
ValueCountFrequency (%)
: 1
50.0%
, 1
50.0%
Space Separator
ValueCountFrequency (%)
9
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 205
74.8%
Latin 69
 
25.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 10
14.5%
t 7
 
10.1%
i 6
 
8.7%
r 5
 
7.2%
n 4
 
5.8%
d 4
 
5.8%
z 4
 
5.8%
u 3
 
4.3%
h 3
 
4.3%
a 2
 
2.9%
Other values (17) 21
30.4%
Common
ValueCountFrequency (%)
0 183
89.3%
9
 
4.4%
1 7
 
3.4%
3 2
 
1.0%
( 1
 
0.5%
: 1
 
0.5%
, 1
 
0.5%
) 1
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 273
99.6%
None 1
 
0.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 183
67.0%
e 10
 
3.7%
9
 
3.3%
t 7
 
2.6%
1 7
 
2.6%
i 6
 
2.2%
r 5
 
1.8%
n 4
 
1.5%
d 4
 
1.5%
z 4
 
1.5%
Other values (24) 34
 
12.5%
None
ValueCountFrequency (%)
ä 1
100.0%

Author: MRT: keine Diszitis, PET Diszitis spinal: overall, 0 = nicht übereinstimmend, 1= übereinstimmend, 2 = MRT unklar, 3 kein MRT, 4 unklar
Categorical

High correlation  Imbalance 

Distinct10
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Memory size12.7 KiB
1
135 
2
30 
3
 
10
0
 
9
4
 
3
Other values (5)
 
5

Length

Max length73
Median length1
Mean length2.3072917
Min length1

Characters and Unicode

Total characters443
Distinct characters49
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)2.6%

Sample

1st row1
2nd row1
3rd row1
4th row2
5th rowkein Handlungsbedarf bei einliegender SI-Schraube 1

Common Values

ValueCountFrequency (%)
1 135
70.3%
2 30
 
15.6%
3 10
 
5.2%
0 9
 
4.7%
4 3
 
1.6%
kein Handlungsbedarf bei einliegender SI-Schraube 1 1
 
0.5%
Author: unklares Ergebnis im PET 3 1
 
0.5%
Author: MRT abgebrochen 3 1
 
0.5%
Author: Patho kein Keim, Mibi S. hämolyticus, V.a. LGI, PET uneindeutig 4 1
 
0.5%
Author: nach Wundrevision erfolgt PET CT, danach wurde Cage explantiert 1 1
 
0.5%

Length

2025-11-23T13:48:01.073533image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-23T13:48:01.183825image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 137
60.6%
2 30
 
13.3%
3 12
 
5.3%
0 9
 
4.0%
author 4
 
1.8%
4 4
 
1.8%
pet 3
 
1.3%
kein 2
 
0.9%
cage 1
 
0.4%
wurde 1
 
0.4%
Other values (23) 23
 
10.2%

Most occurring characters

ValueCountFrequency (%)
1 137
30.9%
34
 
7.7%
2 30
 
6.8%
e 22
 
5.0%
i 16
 
3.6%
n 16
 
3.6%
r 14
 
3.2%
a 12
 
2.7%
3 12
 
2.7%
u 12
 
2.7%
Other values (39) 138
31.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 192
43.3%
Lowercase Letter 172
38.8%
Space Separator 34
 
7.7%
Uppercase Letter 33
 
7.4%
Other Punctuation 11
 
2.5%
Dash Punctuation 1
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 22
12.8%
i 16
 
9.3%
n 16
 
9.3%
r 14
 
8.1%
a 12
 
7.0%
u 12
 
7.0%
t 10
 
5.8%
h 10
 
5.8%
o 9
 
5.2%
d 7
 
4.1%
Other values (14) 44
25.6%
Uppercase Letter
ValueCountFrequency (%)
T 5
15.2%
P 4
12.1%
E 4
12.1%
A 4
12.1%
S 3
9.1%
M 2
 
6.1%
C 2
 
6.1%
I 2
 
6.1%
G 1
 
3.0%
W 1
 
3.0%
Other values (5) 5
15.2%
Decimal Number
ValueCountFrequency (%)
1 137
71.4%
2 30
 
15.6%
3 12
 
6.2%
0 9
 
4.7%
4 4
 
2.1%
Other Punctuation
ValueCountFrequency (%)
: 4
36.4%
, 4
36.4%
. 3
27.3%
Space Separator
ValueCountFrequency (%)
34
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 238
53.7%
Latin 205
46.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 22
 
10.7%
i 16
 
7.8%
n 16
 
7.8%
r 14
 
6.8%
a 12
 
5.9%
u 12
 
5.9%
t 10
 
4.9%
h 10
 
4.9%
o 9
 
4.4%
d 7
 
3.4%
Other values (29) 77
37.6%
Common
ValueCountFrequency (%)
1 137
57.6%
34
 
14.3%
2 30
 
12.6%
3 12
 
5.0%
0 9
 
3.8%
: 4
 
1.7%
, 4
 
1.7%
4 4
 
1.7%
. 3
 
1.3%
- 1
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 442
99.8%
None 1
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 137
31.0%
34
 
7.7%
2 30
 
6.8%
e 22
 
5.0%
i 16
 
3.6%
n 16
 
3.6%
r 14
 
3.2%
a 12
 
2.7%
3 12
 
2.7%
u 12
 
2.7%
Other values (38) 137
31.0%
None
ValueCountFrequency (%)
ä 1
100.0%

initialer Fokus
Text

Distinct69
Distinct (%)35.9%
Missing0
Missing (%)0.0%
Memory size14.9 KiB
2025-11-23T13:48:01.384130image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length58
Median length47
Mean length11.90625
Min length1

Characters and Unicode

Total characters2286
Distinct characters64
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique57 ?
Unique (%)29.7%

Sample

1st rowkein
2nd rowAuthor: 6 Wo sVorOP, Ulcus
3rd rowAuthor: 7 W sVorOP
4th rowPolytox vor 20 a, Zn Diszitis
5th rowsVorOP
ValueCountFrequency (%)
svorop 60
17.1%
kein 59
16.9%
z.n 44
 
12.6%
diszitis 24
 
6.9%
urosepsis 10
 
2.9%
zn 10
 
2.9%
z 5
 
1.4%
n 5
 
1.4%
infekt 4
 
1.1%
endokarditis 4
 
1.1%
Other values (96) 125
35.7%
2025-11-23T13:48:01.707835image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 233
 
10.2%
n 200
 
8.7%
s 197
 
8.6%
e 188
 
8.2%
158
 
6.9%
r 128
 
5.6%
o 111
 
4.9%
t 99
 
4.3%
. 98
 
4.3%
k 82
 
3.6%
Other values (54) 792
34.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1557
68.1%
Uppercase Letter 418
 
18.3%
Space Separator 158
 
6.9%
Other Punctuation 124
 
5.4%
Decimal Number 11
 
0.5%
Dash Punctuation 8
 
0.3%
Open Punctuation 5
 
0.2%
Close Punctuation 5
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 233
15.0%
n 200
12.8%
s 197
12.7%
e 188
12.1%
r 128
8.2%
o 111
7.1%
t 99
6.4%
k 82
 
5.3%
a 48
 
3.1%
z 38
 
2.4%
Other values (18) 233
15.0%
Uppercase Letter
ValueCountFrequency (%)
P 77
18.4%
O 66
15.8%
Z 64
15.3%
V 61
14.6%
D 26
 
6.2%
U 16
 
3.8%
S 15
 
3.6%
H 14
 
3.3%
E 12
 
2.9%
I 11
 
2.6%
Other values (11) 56
13.4%
Decimal Number
ValueCountFrequency (%)
2 3
27.3%
1 2
18.2%
3 2
18.2%
9 1
 
9.1%
7 1
 
9.1%
0 1
 
9.1%
6 1
 
9.1%
Other Punctuation
ValueCountFrequency (%)
. 98
79.0%
, 23
 
18.5%
: 2
 
1.6%
/ 1
 
0.8%
Space Separator
ValueCountFrequency (%)
158
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1975
86.4%
Common 311
 
13.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 233
 
11.8%
n 200
 
10.1%
s 197
 
10.0%
e 188
 
9.5%
r 128
 
6.5%
o 111
 
5.6%
t 99
 
5.0%
k 82
 
4.2%
P 77
 
3.9%
O 66
 
3.3%
Other values (39) 594
30.1%
Common
ValueCountFrequency (%)
158
50.8%
. 98
31.5%
, 23
 
7.4%
- 8
 
2.6%
( 5
 
1.6%
) 5
 
1.6%
2 3
 
1.0%
: 2
 
0.6%
1 2
 
0.6%
3 2
 
0.6%
Other values (5) 5
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2276
99.6%
None 10
 
0.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 233
 
10.2%
n 200
 
8.8%
s 197
 
8.7%
e 188
 
8.3%
158
 
6.9%
r 128
 
5.6%
o 111
 
4.9%
t 99
 
4.3%
. 98
 
4.3%
k 82
 
3.6%
Other values (50) 782
34.4%
None
ValueCountFrequency (%)
ä 3
30.0%
ü 3
30.0%
ß 2
20.0%
ö 2
20.0%

neuer Fokus nach PET
Text

Distinct53
Distinct (%)27.6%
Missing0
Missing (%)0.0%
Memory size15.5 KiB
2025-11-23T13:48:01.876168image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length78
Median length4
Mean length10.46875
Min length4

Characters and Unicode

Total characters2010
Distinct characters59
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique46 ?
Unique (%)24.0%

Sample

1st rowkein
2nd rowkein
3rd rowkein
4th rowkein
5th rowkein
ValueCountFrequency (%)
kein 152
47.9%
nicht 26
 
8.2%
bestätigt 17
 
5.4%
zahn 14
 
4.4%
knie 6
 
1.9%
erkannt 5
 
1.6%
pleuraempyem 4
 
1.3%
diszitis 3
 
0.9%
schulter 3
 
0.9%
endokarditis 3
 
0.9%
Other values (68) 84
26.5%
2025-11-23T13:48:02.232010image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 284
14.1%
e 267
13.3%
n 244
12.1%
k 174
 
8.7%
t 138
 
6.9%
125
 
6.2%
s 93
 
4.6%
h 63
 
3.1%
a 45
 
2.2%
r 44
 
2.2%
Other values (49) 533
26.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1671
83.1%
Space Separator 125
 
6.2%
Uppercase Letter 117
 
5.8%
Close Punctuation 34
 
1.7%
Open Punctuation 34
 
1.7%
Other Punctuation 24
 
1.2%
Dash Punctuation 3
 
0.1%
Decimal Number 2
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 284
17.0%
e 267
16.0%
n 244
14.6%
k 174
10.4%
t 138
8.3%
s 93
 
5.6%
h 63
 
3.8%
a 45
 
2.7%
r 44
 
2.6%
c 42
 
2.5%
Other values (17) 277
16.6%
Uppercase Letter
ValueCountFrequency (%)
Z 14
12.0%
S 11
9.4%
D 11
9.4%
C 11
9.4%
P 10
8.5%
A 9
 
7.7%
K 8
 
6.8%
E 7
 
6.0%
G 7
 
6.0%
H 5
 
4.3%
Other values (11) 24
20.5%
Other Punctuation
ValueCountFrequency (%)
, 18
75.0%
. 2
 
8.3%
" 2
 
8.3%
/ 1
 
4.2%
: 1
 
4.2%
Decimal Number
ValueCountFrequency (%)
3 1
50.0%
4 1
50.0%
Space Separator
ValueCountFrequency (%)
125
100.0%
Close Punctuation
ValueCountFrequency (%)
) 34
100.0%
Open Punctuation
ValueCountFrequency (%)
( 34
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1788
89.0%
Common 222
 
11.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 284
15.9%
e 267
14.9%
n 244
13.6%
k 174
9.7%
t 138
 
7.7%
s 93
 
5.2%
h 63
 
3.5%
a 45
 
2.5%
r 44
 
2.5%
c 42
 
2.3%
Other values (38) 394
22.0%
Common
ValueCountFrequency (%)
125
56.3%
) 34
 
15.3%
( 34
 
15.3%
, 18
 
8.1%
- 3
 
1.4%
. 2
 
0.9%
" 2
 
0.9%
/ 1
 
0.5%
3 1
 
0.5%
: 1
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1986
98.8%
None 24
 
1.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 284
14.3%
e 267
13.4%
n 244
12.3%
k 174
 
8.8%
t 138
 
6.9%
125
 
6.3%
s 93
 
4.7%
h 63
 
3.2%
a 45
 
2.3%
r 44
 
2.2%
Other values (45) 509
25.6%
None
ValueCountFrequency (%)
ä 18
75.0%
ß 3
 
12.5%
ü 2
 
8.3%
Ö 1
 
4.2%

TE at sus focus 2 = vorOP spinal, 0 Fokus nicht dargestellt, 3 kein Fokus, 4 Fokus weg/saniert, 5 Fokus nicht gefunden
Categorical

Distinct11
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Memory size12.5 KiB
3
69 
2
62 
1
27 
4
21 
5
 
5
Other values (6)
8 

Length

Max length48
Median length1
Mean length1.3854167
Min length1

Characters and Unicode

Total characters266
Distinct characters30
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)2.6%

Sample

1st row3
2nd row2
3rd row2
4th row3
5th row2

Common Values

ValueCountFrequency (%)
3 69
35.9%
2 62
32.3%
1 27
 
14.1%
4 21
 
10.9%
5 5
 
2.6%
2, 5 3
 
1.6%
1, 2 1
 
0.5%
Author: V.a. Aortenprothese -> nicht bestätigt 4 1
 
0.5%
3, 5 1
 
0.5%
1, 2, 2005 1
 
0.5%

Length

2025-11-23T13:48:02.348406image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
3 70
34.1%
2 67
32.7%
1 29
14.1%
4 22
 
10.7%
5 9
 
4.4%
author 1
 
0.5%
v.a 1
 
0.5%
aortenprothese 1
 
0.5%
1
 
0.5%
nicht 1
 
0.5%
Other values (3) 3
 
1.5%

Most occurring characters

ValueCountFrequency (%)
3 71
26.7%
2 69
25.9%
1 29
10.9%
4 22
 
8.3%
13
 
4.9%
5 10
 
3.8%
, 7
 
2.6%
t 7
 
2.6%
e 4
 
1.5%
o 3
 
1.1%
Other values (20) 31
11.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 203
76.3%
Lowercase Letter 33
 
12.4%
Space Separator 13
 
4.9%
Other Punctuation 12
 
4.5%
Uppercase Letter 3
 
1.1%
Dash Punctuation 1
 
0.4%
Math Symbol 1
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 7
21.2%
e 4
12.1%
o 3
9.1%
r 3
9.1%
h 3
9.1%
i 2
 
6.1%
n 2
 
6.1%
s 2
 
6.1%
g 1
 
3.0%
ä 1
 
3.0%
Other values (5) 5
15.2%
Decimal Number
ValueCountFrequency (%)
3 71
35.0%
2 69
34.0%
1 29
14.3%
4 22
 
10.8%
5 10
 
4.9%
0 2
 
1.0%
Other Punctuation
ValueCountFrequency (%)
, 7
58.3%
. 3
25.0%
: 1
 
8.3%
/ 1
 
8.3%
Uppercase Letter
ValueCountFrequency (%)
A 2
66.7%
V 1
33.3%
Space Separator
ValueCountFrequency (%)
13
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%
Math Symbol
ValueCountFrequency (%)
> 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 230
86.5%
Latin 36
 
13.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 7
19.4%
e 4
11.1%
o 3
8.3%
r 3
8.3%
h 3
8.3%
i 2
 
5.6%
A 2
 
5.6%
n 2
 
5.6%
s 2
 
5.6%
g 1
 
2.8%
Other values (7) 7
19.4%
Common
ValueCountFrequency (%)
3 71
30.9%
2 69
30.0%
1 29
12.6%
4 22
 
9.6%
13
 
5.7%
5 10
 
4.3%
, 7
 
3.0%
. 3
 
1.3%
0 2
 
0.9%
- 1
 
0.4%
Other values (3) 3
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 265
99.6%
None 1
 
0.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 71
26.8%
2 69
26.0%
1 29
10.9%
4 22
 
8.3%
13
 
4.9%
5 10
 
3.8%
, 7
 
2.6%
t 7
 
2.6%
e 4
 
1.5%
o 3
 
1.1%
Other values (19) 30
11.3%
None
ValueCountFrequency (%)
ä 1
100.0%

add TE
Categorical

High correlation 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size12.8 KiB
0.0
133 
1.0
59 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters576
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 133
69.3%
1.0 59
30.7%

Length

2025-11-23T13:48:02.457615image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-23T13:48:02.530296image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 133
69.3%
1.0 59
30.7%

Most occurring characters

ValueCountFrequency (%)
0 325
56.4%
. 192
33.3%
1 59
 
10.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 384
66.7%
Other Punctuation 192
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 325
84.6%
1 59
 
15.4%
Other Punctuation
ValueCountFrequency (%)
. 192
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 576
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 325
56.4%
. 192
33.3%
1 59
 
10.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 576
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 325
56.4%
. 192
33.3%
1 59
 
10.2%

add TE 1 = new focus 2=TE nicht relevant und tatsächlich nicht relevant 3 = nicht untersucht 4 = kein Fokus 5 = bekannter Fokus, schon behandelt 6 Tumor 0 no add TE
Categorical

High correlation  Imbalance 

Distinct15
Distinct (%)7.8%
Missing0
Missing (%)0.0%
Memory size12.8 KiB
0
131 
1
22 
4
16 
3
 
6
5
 
4
Other values (10)
 
13

Length

Max length105
Median length1
Mean length2.484375
Min length1

Characters and Unicode

Total characters477
Distinct characters48
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)4.2%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 131
68.2%
1 22
 
11.5%
4 16
 
8.3%
3 6
 
3.1%
5 4
 
2.1%
2 3
 
1.6%
6 2
 
1.0%
Author: Colitis als unspez gewertet (unklar ob klinisch iV kontrolliert), Sinusitis ohne TE, kein Fokus 2 1
 
0.5%
Author: Colitis als relevant eingestuft, aber nicht bestätigt 3 1
 
0.5%
Author: unsicher: Knie, Gelenk 1 1
 
0.5%
Other values (5) 5
 
2.6%

Length

2025-11-23T13:48:02.899093image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0 131
56.5%
1 25
 
10.8%
4 17
 
7.3%
3 8
 
3.4%
5 7
 
3.0%
author 5
 
2.2%
2 4
 
1.7%
6 3
 
1.3%
colitis 2
 
0.9%
als 2
 
0.9%
Other values (28) 28
 
12.1%

Most occurring characters

ValueCountFrequency (%)
0 131
27.5%
40
 
8.4%
e 26
 
5.5%
1 25
 
5.2%
t 24
 
5.0%
i 21
 
4.4%
n 18
 
3.8%
r 17
 
3.6%
4 17
 
3.6%
s 14
 
2.9%
Other values (38) 144
30.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 203
42.6%
Decimal Number 195
40.9%
Space Separator 40
 
8.4%
Uppercase Letter 22
 
4.6%
Other Punctuation 15
 
3.1%
Close Punctuation 1
 
0.2%
Open Punctuation 1
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 26
12.8%
t 24
11.8%
i 21
10.3%
n 18
8.9%
r 17
8.4%
s 14
 
6.9%
o 13
 
6.4%
u 12
 
5.9%
h 12
 
5.9%
l 11
 
5.4%
Other values (12) 35
17.2%
Uppercase Letter
ValueCountFrequency (%)
A 7
31.8%
C 2
 
9.1%
P 2
 
9.1%
B 1
 
4.5%
W 1
 
4.5%
Z 1
 
4.5%
G 1
 
4.5%
K 1
 
4.5%
F 1
 
4.5%
E 1
 
4.5%
Other values (4) 4
18.2%
Decimal Number
ValueCountFrequency (%)
0 131
67.2%
1 25
 
12.8%
4 17
 
8.7%
3 8
 
4.1%
5 7
 
3.6%
2 4
 
2.1%
6 3
 
1.5%
Other Punctuation
ValueCountFrequency (%)
, 9
60.0%
: 6
40.0%
Space Separator
ValueCountFrequency (%)
40
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 252
52.8%
Latin 225
47.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 26
11.6%
t 24
10.7%
i 21
 
9.3%
n 18
 
8.0%
r 17
 
7.6%
s 14
 
6.2%
o 13
 
5.8%
u 12
 
5.3%
h 12
 
5.3%
l 11
 
4.9%
Other values (26) 57
25.3%
Common
ValueCountFrequency (%)
0 131
52.0%
40
 
15.9%
1 25
 
9.9%
4 17
 
6.7%
, 9
 
3.6%
3 8
 
3.2%
5 7
 
2.8%
: 6
 
2.4%
2 4
 
1.6%
6 3
 
1.2%
Other values (2) 2
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 475
99.6%
None 2
 
0.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 131
27.6%
40
 
8.4%
e 26
 
5.5%
1 25
 
5.3%
t 24
 
5.1%
i 21
 
4.4%
n 18
 
3.8%
r 17
 
3.6%
4 17
 
3.6%
s 14
 
2.9%
Other values (36) 142
29.9%
None
ValueCountFrequency (%)
ü 1
50.0%
ä 1
50.0%

nonspinal: overall 2 unklar 3 nicht abklärt 0 nicht übereinstimmend 4 ausgeheilt nach Behandlung 5 nicht abgebildet
Categorical

High correlation  Imbalance 

Distinct13
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Memory size12.8 KiB
1
150 
0
22 
4
 
7
3
 
4
Author: Sinusitis ja (ohne TE), aber kein Fokus 1
 
1
Other values (8)
 
8

Length

Max length58
Median length1
Mean length2.609375
Min length1

Characters and Unicode

Total characters501
Distinct characters48
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)4.7%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 150
78.1%
0 22
 
11.5%
4 7
 
3.6%
3 4
 
2.1%
Author: Sinusitis ja (ohne TE), aber kein Fokus 1 1
 
0.5%
Author: Extremitäten nicht mitabgebildet 5 1
 
0.5%
Author: Bursitis, aber kein Infektfokus 0 1
 
0.5%
Author: DD Prostatitis DD CA -> war CA 1 1
 
0.5%
Author: Füße nicht im PET 1 1
 
0.5%
Author: Endokarditis nicht erkannt, Zahnfokus erkannt 1, 0 1
 
0.5%
Other values (3) 3
 
1.6%

Length

2025-11-23T13:48:03.035647image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1 155
65.1%
0 26
 
10.9%
author 8
 
3.4%
4 7
 
2.9%
nicht 5
 
2.1%
3 4
 
1.7%
erkannt 4
 
1.7%
ca 2
 
0.8%
dd 2
 
0.8%
kein 2
 
0.8%
Other values (22) 23
 
9.7%

Most occurring characters

ValueCountFrequency (%)
1 155
30.9%
46
 
9.2%
t 29
 
5.8%
0 26
 
5.2%
n 23
 
4.6%
i 20
 
4.0%
r 19
 
3.8%
h 17
 
3.4%
e 16
 
3.2%
o 14
 
2.8%
Other values (38) 136
27.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 212
42.3%
Decimal Number 194
38.7%
Space Separator 46
 
9.2%
Uppercase Letter 32
 
6.4%
Other Punctuation 13
 
2.6%
Close Punctuation 1
 
0.2%
Math Symbol 1
 
0.2%
Dash Punctuation 1
 
0.2%
Open Punctuation 1
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 29
13.7%
n 23
10.8%
i 20
9.4%
r 19
9.0%
h 17
8.0%
e 16
7.5%
o 14
 
6.6%
u 13
 
6.1%
a 13
 
6.1%
k 11
 
5.2%
Other values (14) 37
17.5%
Uppercase Letter
ValueCountFrequency (%)
A 10
31.2%
E 4
 
12.5%
D 4
 
12.5%
Z 3
 
9.4%
F 2
 
6.2%
C 2
 
6.2%
T 2
 
6.2%
P 2
 
6.2%
B 1
 
3.1%
I 1
 
3.1%
Decimal Number
ValueCountFrequency (%)
1 155
79.9%
0 26
 
13.4%
4 7
 
3.6%
3 4
 
2.1%
5 1
 
0.5%
2 1
 
0.5%
Other Punctuation
ValueCountFrequency (%)
: 8
61.5%
, 5
38.5%
Space Separator
ValueCountFrequency (%)
46
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Math Symbol
ValueCountFrequency (%)
> 1
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 257
51.3%
Latin 244
48.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 29
11.9%
n 23
 
9.4%
i 20
 
8.2%
r 19
 
7.8%
h 17
 
7.0%
e 16
 
6.6%
o 14
 
5.7%
u 13
 
5.3%
a 13
 
5.3%
k 11
 
4.5%
Other values (25) 69
28.3%
Common
ValueCountFrequency (%)
1 155
60.3%
46
 
17.9%
0 26
 
10.1%
: 8
 
3.1%
4 7
 
2.7%
, 5
 
1.9%
3 4
 
1.6%
) 1
 
0.4%
> 1
 
0.4%
- 1
 
0.4%
Other values (3) 3
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 497
99.2%
None 4
 
0.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 155
31.2%
46
 
9.3%
t 29
 
5.8%
0 26
 
5.2%
n 23
 
4.6%
i 20
 
4.0%
r 19
 
3.8%
h 17
 
3.4%
e 16
 
3.2%
o 14
 
2.8%
Other values (35) 132
26.6%
None
ValueCountFrequency (%)
ä 2
50.0%
ß 1
25.0%
ü 1
25.0%

reason for PET
Categorical

High correlation  Imbalance 

Distinct22
Distinct (%)11.5%
Missing0
Missing (%)0.0%
Memory size14.9 KiB
Fokussuche
146 
Ausschluss Diszitis
 
14
Fokussuche, Ausschluss Diszitis
 
6
Fokussuche, Nachweis Diszitis
 
5
Fokussuche, Ausschluss Diszitis, MRT Artefakt
 
2
Other values (17)
19 

Length

Max length68
Median length10
Mean length13.901042
Min length10

Characters and Unicode

Total characters2669
Distinct characters47
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15 ?
Unique (%)7.8%

Sample

1st rowFokussuche
2nd rowFokussuche
3rd rowFokussuche
4th rowAusschluss Diszitis
5th rowFokussuche

Common Values

ValueCountFrequency (%)
Fokussuche 146
76.0%
Ausschluss Diszitis 14
 
7.3%
Fokussuche, Ausschluss Diszitis 6
 
3.1%
Fokussuche, Nachweis Diszitis 5
 
2.6%
Fokussuche, Ausschluss Diszitis, MRT Artefakt 2
 
1.0%
Nachweis Diszitis 2
 
1.0%
MRT-Ersatz 2
 
1.0%
Fokussuche, MRT DD Tumor 1
 
0.5%
Ausschluss Diszitis, Fokussuche 1
 
0.5%
Fokussuche, MRT Ersatz 1
 
0.5%
Other values (12) 12
 
6.2%

Length

2025-11-23T13:48:03.168068image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
fokussuche 165
59.4%
diszitis 38
 
13.7%
ausschluss 27
 
9.7%
nachweis 9
 
3.2%
mrt 6
 
2.2%
bei 4
 
1.4%
artefakt 3
 
1.1%
mrt-ersatz 2
 
0.7%
b 2
 
0.7%
mrt-unfähigkeit 2
 
0.7%
Other values (19) 20
 
7.2%

Most occurring characters

ValueCountFrequency (%)
s 531
19.9%
u 387
14.5%
h 207
 
7.8%
c 203
 
7.6%
e 202
 
7.6%
k 176
 
6.6%
o 172
 
6.4%
F 165
 
6.2%
i 138
 
5.2%
86
 
3.2%
Other values (37) 402
15.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2253
84.4%
Uppercase Letter 299
 
11.2%
Space Separator 86
 
3.2%
Other Punctuation 24
 
0.9%
Dash Punctuation 4
 
0.1%
Close Punctuation 1
 
< 0.1%
Open Punctuation 1
 
< 0.1%
Decimal Number 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 531
23.6%
u 387
17.2%
h 207
 
9.2%
c 203
 
9.0%
e 202
 
9.0%
k 176
 
7.8%
o 172
 
7.6%
i 138
 
6.1%
t 58
 
2.6%
z 41
 
1.8%
Other values (14) 138
 
6.1%
Uppercase Letter
ValueCountFrequency (%)
F 165
55.2%
D 42
 
14.0%
A 30
 
10.0%
T 13
 
4.3%
M 12
 
4.0%
R 11
 
3.7%
N 9
 
3.0%
S 4
 
1.3%
E 3
 
1.0%
H 2
 
0.7%
Other values (6) 8
 
2.7%
Other Punctuation
ValueCountFrequency (%)
, 22
91.7%
. 2
 
8.3%
Space Separator
ValueCountFrequency (%)
86
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Decimal Number
ValueCountFrequency (%)
2 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2552
95.6%
Common 117
 
4.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 531
20.8%
u 387
15.2%
h 207
 
8.1%
c 203
 
8.0%
e 202
 
7.9%
k 176
 
6.9%
o 172
 
6.7%
F 165
 
6.5%
i 138
 
5.4%
t 58
 
2.3%
Other values (30) 313
12.3%
Common
ValueCountFrequency (%)
86
73.5%
, 22
 
18.8%
- 4
 
3.4%
. 2
 
1.7%
) 1
 
0.9%
( 1
 
0.9%
2 1
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2666
99.9%
None 3
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 531
19.9%
u 387
14.5%
h 207
 
7.8%
c 203
 
7.6%
e 202
 
7.6%
k 176
 
6.6%
o 172
 
6.5%
F 165
 
6.2%
i 138
 
5.2%
86
 
3.2%
Other values (35) 399
15.0%
None
ValueCountFrequency (%)
ä 2
66.7%
ö 1
33.3%

1 = Fokussuche, 2 = Ausschluss/Nachweis Diszitis weil MRT unklar, 3 = MRT nicht möglich / Ersatz für MRT, 4 = VK, 5 = Materialschaden, Frage nach Infekt, 6 = Infekt im Labor ohne Fokus
Categorical

High correlation  Imbalance 

Distinct10
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Memory size12.5 KiB
1
146 
2
20 
1, 2
 
13
3
 
4
1, 3
 
3
Other values (5)
 
6

Length

Max length18
Median length1
Mean length1.4739583
Min length1

Characters and Unicode

Total characters283
Distinct characters18
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)2.1%

Sample

1st row1
2nd row1
3rd row1
4th row2
5th row1

Common Values

ValueCountFrequency (%)
1 146
76.0%
2 20
 
10.4%
1, 2 13
 
6.8%
3 4
 
2.1%
1, 3 3
 
1.6%
4 2
 
1.0%
1, 2, 2006 1
 
0.5%
3, 4 1
 
0.5%
3, 6, (2 kein MRT) 1
 
0.5%
2 (kein MRT), 5 1
 
0.5%

Length

2025-11-23T13:48:03.295757image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-23T13:48:03.399900image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 163
74.8%
2 36
 
16.5%
3 9
 
4.1%
4 3
 
1.4%
kein 2
 
0.9%
mrt 2
 
0.9%
2006 1
 
0.5%
6 1
 
0.5%
5 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
1 163
57.6%
2 37
 
13.1%
26
 
9.2%
, 22
 
7.8%
3 9
 
3.2%
4 3
 
1.1%
n 2
 
0.7%
) 2
 
0.7%
T 2
 
0.7%
R 2
 
0.7%
Other values (8) 15
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 217
76.7%
Space Separator 26
 
9.2%
Other Punctuation 22
 
7.8%
Lowercase Letter 8
 
2.8%
Uppercase Letter 6
 
2.1%
Close Punctuation 2
 
0.7%
Open Punctuation 2
 
0.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 163
75.1%
2 37
 
17.1%
3 9
 
4.1%
4 3
 
1.4%
6 2
 
0.9%
0 2
 
0.9%
5 1
 
0.5%
Lowercase Letter
ValueCountFrequency (%)
n 2
25.0%
k 2
25.0%
i 2
25.0%
e 2
25.0%
Uppercase Letter
ValueCountFrequency (%)
T 2
33.3%
R 2
33.3%
M 2
33.3%
Space Separator
ValueCountFrequency (%)
26
100.0%
Other Punctuation
ValueCountFrequency (%)
, 22
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 269
95.1%
Latin 14
 
4.9%

Most frequent character per script

Common
ValueCountFrequency (%)
1 163
60.6%
2 37
 
13.8%
26
 
9.7%
, 22
 
8.2%
3 9
 
3.3%
4 3
 
1.1%
) 2
 
0.7%
( 2
 
0.7%
6 2
 
0.7%
0 2
 
0.7%
Latin
ValueCountFrequency (%)
n 2
14.3%
T 2
14.3%
R 2
14.3%
M 2
14.3%
k 2
14.3%
i 2
14.3%
e 2
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 283
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 163
57.6%
2 37
 
13.1%
26
 
9.2%
, 22
 
7.8%
3 9
 
3.2%
4 3
 
1.1%
n 2
 
0.7%
) 2
 
0.7%
T 2
 
0.7%
R 2
 
0.7%
Other values (8) 15
 
5.3%

Author: (1= DM, 2=iv Drogen, 3=PAVK, 4=Cortisontherapie, 5=Zahnbehandlung, 6=Immunschwäche, 7=zn CTX, 8=Malignom, 9=Infiltrationen, 10=C2) Risikofaktoren
Categorical

Distinct44
Distinct (%)22.9%
Missing0
Missing (%)0.0%
Memory size12.7 KiB
0
56 
3
22 
1
18 
1, 3
13 
8
9 
Other values (39)
74 

Length

Max length21
Median length1
Mean length2.890625
Min length1

Characters and Unicode

Total characters555
Distinct characters26
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24 ?
Unique (%)12.5%

Sample

1st row1
2nd row4
3rd row7,8
4th row2
5th row0

Common Values

ValueCountFrequency (%)
0 56
29.2%
3 22
 
11.5%
1 18
 
9.4%
1, 3 13
 
6.8%
8 9
 
4.7%
2 8
 
4.2%
7, 8 7
 
3.6%
4 7
 
3.6%
4, 6 4
 
2.1%
3, 7, 2008 4
 
2.1%
Other values (34) 44
22.9%

Length

2025-11-23T13:48:03.557355image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0 58
21.1%
3 54
19.6%
1 41
14.9%
8 23
 
8.4%
4 18
 
6.5%
7 16
 
5.8%
6 13
 
4.7%
2 8
 
2.9%
2008 8
 
2.9%
12 6
 
2.2%
Other values (13) 30
10.9%

Most occurring characters

ValueCountFrequency (%)
0 88
15.9%
, 86
15.5%
83
15.0%
1 58
10.5%
3 58
10.5%
8 32
 
5.8%
2 28
 
5.0%
4 20
 
3.6%
7 17
 
3.1%
6 15
 
2.7%
Other values (16) 70
12.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 328
59.1%
Other Punctuation 86
 
15.5%
Space Separator 83
 
15.0%
Lowercase Letter 47
 
8.5%
Uppercase Letter 7
 
1.3%
Open Punctuation 2
 
0.4%
Close Punctuation 2
 
0.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 88
26.8%
1 58
17.7%
3 58
17.7%
8 32
 
9.8%
2 28
 
8.5%
4 20
 
6.1%
7 17
 
5.2%
6 15
 
4.6%
9 8
 
2.4%
5 4
 
1.2%
Lowercase Letter
ValueCountFrequency (%)
e 12
25.5%
r 12
25.5%
i 4
 
8.5%
s 4
 
8.5%
o 4
 
8.5%
b 4
 
8.5%
z 4
 
8.5%
h 3
 
6.4%
Uppercase Letter
ValueCountFrequency (%)
L 4
57.1%
H 1
 
14.3%
P 1
 
14.3%
C 1
 
14.3%
Other Punctuation
ValueCountFrequency (%)
, 86
100.0%
Space Separator
ValueCountFrequency (%)
83
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 501
90.3%
Latin 54
 
9.7%

Most frequent character per script

Common
ValueCountFrequency (%)
0 88
17.6%
, 86
17.2%
83
16.6%
1 58
11.6%
3 58
11.6%
8 32
 
6.4%
2 28
 
5.6%
4 20
 
4.0%
7 17
 
3.4%
6 15
 
3.0%
Other values (4) 16
 
3.2%
Latin
ValueCountFrequency (%)
e 12
22.2%
r 12
22.2%
i 4
 
7.4%
s 4
 
7.4%
o 4
 
7.4%
b 4
 
7.4%
z 4
 
7.4%
L 4
 
7.4%
h 3
 
5.6%
H 1
 
1.9%
Other values (2) 2
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 555
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 88
15.9%
, 86
15.5%
83
15.0%
1 58
10.5%
3 58
10.5%
8 32
 
5.8%
2 28
 
5.0%
4 20
 
3.6%
7 17
 
3.1%
6 15
 
2.7%
Other values (16) 70
12.6%

RevisionsOP 2 =kein Infekt
Categorical

Imbalance 

Distinct6
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size12.4 KiB
0
165 
2
 
12
1
 
12
Author: 1
 
1
Author: nekrotisierende WHST 1
 
1

Length

Max length30
Median length1
Mean length1.265625
Min length1

Characters and Unicode

Total characters243
Distinct characters21
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)1.6%

Sample

1st rowAuthor: 1
2nd row2
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 165
85.9%
2 12
 
6.2%
1 12
 
6.2%
Author: 1 1
 
0.5%
Author: nekrotisierende WHST 1 1
 
0.5%
Author: WHST 1 1
 
0.5%

Length

2025-11-23T13:48:03.689138image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-23T13:48:03.785306image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 165
83.3%
1 15
 
7.6%
2 12
 
6.1%
author 3
 
1.5%
whst 2
 
1.0%
nekrotisierende 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 165
67.9%
1 15
 
6.2%
2 12
 
4.9%
7
 
2.9%
r 5
 
2.1%
e 4
 
1.6%
t 4
 
1.6%
o 4
 
1.6%
A 3
 
1.2%
u 3
 
1.2%
Other values (11) 21
 
8.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 192
79.0%
Lowercase Letter 30
 
12.3%
Uppercase Letter 11
 
4.5%
Space Separator 7
 
2.9%
Other Punctuation 3
 
1.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 5
16.7%
e 4
13.3%
t 4
13.3%
o 4
13.3%
u 3
10.0%
h 3
10.0%
n 2
 
6.7%
i 2
 
6.7%
k 1
 
3.3%
s 1
 
3.3%
Uppercase Letter
ValueCountFrequency (%)
A 3
27.3%
W 2
18.2%
H 2
18.2%
S 2
18.2%
T 2
18.2%
Decimal Number
ValueCountFrequency (%)
0 165
85.9%
1 15
 
7.8%
2 12
 
6.2%
Space Separator
ValueCountFrequency (%)
7
100.0%
Other Punctuation
ValueCountFrequency (%)
: 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 202
83.1%
Latin 41
 
16.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 5
12.2%
e 4
9.8%
t 4
9.8%
o 4
9.8%
A 3
 
7.3%
u 3
 
7.3%
h 3
 
7.3%
n 2
 
4.9%
i 2
 
4.9%
W 2
 
4.9%
Other values (6) 9
22.0%
Common
ValueCountFrequency (%)
0 165
81.7%
1 15
 
7.4%
2 12
 
5.9%
7
 
3.5%
: 3
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 243
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 165
67.9%
1 15
 
6.2%
2 12
 
4.9%
7
 
2.9%
r 5
 
2.1%
e 4
 
1.6%
t 4
 
1.6%
o 4
 
1.6%
A 3
 
1.2%
u 3
 
1.2%
Other values (11) 21
 
8.6%

ASA
Categorical

Distinct4
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size12.8 KiB
3.0
116 
2.0
62 
4.0
 
10
1.0
 
4

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters576
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row3.0
3rd row2.0
4th row3.0
5th row2.0

Common Values

ValueCountFrequency (%)
3.0 116
60.4%
2.0 62
32.3%
4.0 10
 
5.2%
1.0 4
 
2.1%

Length

2025-11-23T13:48:03.925605image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-23T13:48:03.995830image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3.0 116
60.4%
2.0 62
32.3%
4.0 10
 
5.2%
1.0 4
 
2.1%

Most occurring characters

ValueCountFrequency (%)
. 192
33.3%
0 192
33.3%
3 116
20.1%
2 62
 
10.8%
4 10
 
1.7%
1 4
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 384
66.7%
Other Punctuation 192
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 192
50.0%
3 116
30.2%
2 62
 
16.1%
4 10
 
2.6%
1 4
 
1.0%
Other Punctuation
ValueCountFrequency (%)
. 192
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 576
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 192
33.3%
0 192
33.3%
3 116
20.1%
2 62
 
10.8%
4 10
 
1.7%
1 4
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 576
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 192
33.3%
0 192
33.3%
3 116
20.1%
2 62
 
10.8%
4 10
 
1.7%
1 4
 
0.7%

ausgeheilt 2=NA 3=dead
Categorical

Distinct7
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size12.4 KiB
1
100 
2
57 
3
13 
x
12 
0
 
8
Other values (2)
 
2

Length

Max length3
Median length1
Mean length1.015625
Min length1

Characters and Unicode

Total characters195
Distinct characters7
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.0%

Sample

1st row1
2nd row1
3rd row1
4th rowx
5th row1

Common Values

ValueCountFrequency (%)
1 100
52.1%
2 57
29.7%
3 13
 
6.8%
x 12
 
6.2%
0 8
 
4.2%
x/1 1
 
0.5%
1? 1
 
0.5%

Length

2025-11-23T13:48:04.095391image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-23T13:48:04.182202image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 101
52.6%
2 57
29.7%
3 13
 
6.8%
x 12
 
6.2%
0 8
 
4.2%
x/1 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
1 102
52.3%
2 57
29.2%
3 13
 
6.7%
x 13
 
6.7%
0 8
 
4.1%
/ 1
 
0.5%
? 1
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 180
92.3%
Lowercase Letter 13
 
6.7%
Other Punctuation 2
 
1.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 102
56.7%
2 57
31.7%
3 13
 
7.2%
0 8
 
4.4%
Other Punctuation
ValueCountFrequency (%)
/ 1
50.0%
? 1
50.0%
Lowercase Letter
ValueCountFrequency (%)
x 13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 182
93.3%
Latin 13
 
6.7%

Most frequent character per script

Common
ValueCountFrequency (%)
1 102
56.0%
2 57
31.3%
3 13
 
7.1%
0 8
 
4.4%
/ 1
 
0.5%
? 1
 
0.5%
Latin
ValueCountFrequency (%)
x 13
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 195
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 102
52.3%
2 57
29.2%
3 13
 
6.7%
x 13
 
6.7%
0 8
 
4.1%
/ 1
 
0.5%
? 1
 
0.5%

Neurologie 1 = Paresen, 2 = vorbestehend, 3 = Tetraparese
Categorical

Missing 

Distinct4
Distinct (%)5.7%
Missing122
Missing (%)63.5%
Memory size12.3 KiB
0.0
53 
2.0
8 
1.0
 
5
3.0
 
4

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters210
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 53
27.6%
2.0 8
 
4.2%
1.0 5
 
2.6%
3.0 4
 
2.1%
(Missing) 122
63.5%

Length

2025-11-23T13:48:04.281824image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-23T13:48:04.348746image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 53
75.7%
2.0 8
 
11.4%
1.0 5
 
7.1%
3.0 4
 
5.7%

Most occurring characters

ValueCountFrequency (%)
0 123
58.6%
. 70
33.3%
2 8
 
3.8%
1 5
 
2.4%
3 4
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 140
66.7%
Other Punctuation 70
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 123
87.9%
2 8
 
5.7%
1 5
 
3.6%
3 4
 
2.9%
Other Punctuation
ValueCountFrequency (%)
. 70
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 210
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 123
58.6%
. 70
33.3%
2 8
 
3.8%
1 5
 
2.4%
3 4
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 123
58.6%
. 70
33.3%
2 8
 
3.8%
1 5
 
2.4%
3 4
 
1.9%

Besserung 1 = komplett 2 =ja, aber nicht auf normal 3 tot 4 neues Defizit
Categorical

Missing 

Distinct5
Distinct (%)7.8%
Missing128
Missing (%)66.7%
Memory size12.1 KiB
x
50 
3
 
5
2
 
4
4
 
3
0
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters64
Distinct characters5
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row3
3rd rowx
4th rowx
5th rowx

Common Values

ValueCountFrequency (%)
x 50
 
26.0%
3 5
 
2.6%
2 4
 
2.1%
4 3
 
1.6%
0 2
 
1.0%
(Missing) 128
66.7%

Length

2025-11-23T13:48:04.448514image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-23T13:48:04.534987image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
x 50
78.1%
3 5
 
7.8%
2 4
 
6.2%
4 3
 
4.7%
0 2
 
3.1%

Most occurring characters

ValueCountFrequency (%)
x 50
78.1%
3 5
 
7.8%
2 4
 
6.2%
4 3
 
4.7%
0 2
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 50
78.1%
Decimal Number 14
 
21.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 5
35.7%
2 4
28.6%
4 3
21.4%
0 2
 
14.3%
Lowercase Letter
ValueCountFrequency (%)
x 50
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 50
78.1%
Common 14
 
21.9%

Most frequent character per script

Common
ValueCountFrequency (%)
3 5
35.7%
2 4
28.6%
4 3
21.4%
0 2
 
14.3%
Latin
ValueCountFrequency (%)
x 50
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 64
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
x 50
78.1%
3 5
 
7.8%
2 4
 
6.2%
4 3
 
4.7%
0 2
 
3.1%

(1= DM, 2=iv Drogen, 3=PAVK, 4=Cortisontherapie, 5=Zahnbehandlung, 6=Immunschwäche, 7=zn CTX, 8=Malignom, 9=Infiltrationen, 10=C2, 11=Parkinson, 12 = Niereninsuffizienz)
Text

Missing 

Distinct9
Distinct (%)75.0%
Missing180
Missing (%)93.8%
Memory size8.4 KiB
2025-11-23T13:48:04.658263image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length60
Median length39.5
Mean length29.75
Min length13

Characters and Unicode

Total characters357
Distinct characters50
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)66.7%

Sample

1st rowMRT im Verlauf in Narkose postop ergänzt
2nd rowvorbestehender sensibler Querschnitt
3rd rowLäsion im PET neg, OP Befunde auch neg
4th rowMRT nicht mit PET und Befund einstimmig
5th row3x PET für Struma, 1x PET danach Ortho Ausschluss Entzündung
ValueCountFrequency (%)
1x 7
 
11.7%
pet 4
 
6.7%
spect 4
 
6.7%
herz 4
 
6.7%
mrt 2
 
3.3%
im 2
 
3.3%
neg 2
 
3.3%
struma 1
 
1.7%
danach 1
 
1.7%
ortho 1
 
1.7%
Other values (32) 32
53.3%
2025-11-23T13:48:04.875524image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
48
 
13.4%
e 27
 
7.6%
n 21
 
5.9%
t 20
 
5.6%
i 18
 
5.0%
s 17
 
4.8%
r 17
 
4.8%
h 14
 
3.9%
c 12
 
3.4%
o 11
 
3.1%
Other values (40) 152
42.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 241
67.5%
Uppercase Letter 52
 
14.6%
Space Separator 48
 
13.4%
Decimal Number 9
 
2.5%
Other Punctuation 5
 
1.4%
Open Punctuation 1
 
0.3%
Close Punctuation 1
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 27
 
11.2%
n 21
 
8.7%
t 20
 
8.3%
i 18
 
7.5%
s 17
 
7.1%
r 17
 
7.1%
h 14
 
5.8%
c 12
 
5.0%
o 11
 
4.6%
a 11
 
4.6%
Other values (16) 73
30.3%
Uppercase Letter
ValueCountFrequency (%)
E 7
13.5%
P 7
13.5%
T 7
13.5%
S 6
11.5%
M 3
 
5.8%
D 3
 
5.8%
O 3
 
5.8%
N 3
 
5.8%
K 2
 
3.8%
A 2
 
3.8%
Other values (7) 9
17.3%
Decimal Number
ValueCountFrequency (%)
1 7
77.8%
3 1
 
11.1%
5 1
 
11.1%
Space Separator
ValueCountFrequency (%)
48
100.0%
Other Punctuation
ValueCountFrequency (%)
, 5
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 293
82.1%
Common 64
 
17.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 27
 
9.2%
n 21
 
7.2%
t 20
 
6.8%
i 18
 
6.1%
s 17
 
5.8%
r 17
 
5.8%
h 14
 
4.8%
c 12
 
4.1%
o 11
 
3.8%
a 11
 
3.8%
Other values (33) 125
42.7%
Common
ValueCountFrequency (%)
48
75.0%
1 7
 
10.9%
, 5
 
7.8%
3 1
 
1.6%
5 1
 
1.6%
( 1
 
1.6%
) 1
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 353
98.9%
None 4
 
1.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
48
 
13.6%
e 27
 
7.6%
n 21
 
5.9%
t 20
 
5.7%
i 18
 
5.1%
s 17
 
4.8%
r 17
 
4.8%
h 14
 
4.0%
c 12
 
3.4%
o 11
 
3.1%
Other values (38) 148
41.9%
None
ValueCountFrequency (%)
ü 2
50.0%
ä 2
50.0%

Unnamed: 45
Text

Missing 

Distinct15
Distinct (%)71.4%
Missing171
Missing (%)89.1%
Memory size9.2 KiB
2025-11-23T13:48:04.995009image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length90
Median length50
Mean length31.666667
Min length9

Characters and Unicode

Total characters665
Distinct characters50
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)57.1%

Sample

1st rowein PET Onko zusätzlich davor
2nd rowein PET Onko zusätzlich davor
3rd rowKnochenPET danach
4th rowDD Rheuma
5th rowRediszitis
ValueCountFrequency (%)
pet 8
 
7.8%
nicht 6
 
5.8%
davor 6
 
5.8%
1x 5
 
4.9%
diszitis 5
 
4.9%
erkannt 5
 
4.9%
herzspect 4
 
3.9%
im 4
 
3.9%
zähne 3
 
2.9%
onko 3
 
2.9%
Other values (41) 54
52.4%
2025-11-23T13:48:05.300420image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
82
 
12.3%
i 58
 
8.7%
n 49
 
7.4%
e 42
 
6.3%
t 36
 
5.4%
r 31
 
4.7%
s 27
 
4.1%
a 27
 
4.1%
h 24
 
3.6%
o 21
 
3.2%
Other values (40) 268
40.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 458
68.9%
Uppercase Letter 101
 
15.2%
Space Separator 82
 
12.3%
Decimal Number 8
 
1.2%
Other Punctuation 7
 
1.1%
Close Punctuation 3
 
0.5%
Math Symbol 3
 
0.5%
Open Punctuation 3
 
0.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 58
12.7%
n 49
 
10.7%
e 42
 
9.2%
t 36
 
7.9%
r 31
 
6.8%
s 27
 
5.9%
a 27
 
5.9%
h 24
 
5.2%
o 21
 
4.6%
z 19
 
4.1%
Other values (15) 124
27.1%
Uppercase Letter
ValueCountFrequency (%)
E 20
19.8%
P 19
18.8%
T 17
16.8%
D 8
 
7.9%
H 5
 
5.0%
K 4
 
4.0%
O 4
 
4.0%
C 4
 
4.0%
S 4
 
4.0%
M 4
 
4.0%
Other values (6) 12
11.9%
Decimal Number
ValueCountFrequency (%)
1 5
62.5%
4 1
 
12.5%
2 1
 
12.5%
5 1
 
12.5%
Space Separator
ValueCountFrequency (%)
82
100.0%
Other Punctuation
ValueCountFrequency (%)
, 7
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Math Symbol
ValueCountFrequency (%)
+ 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 559
84.1%
Common 106
 
15.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 58
 
10.4%
n 49
 
8.8%
e 42
 
7.5%
t 36
 
6.4%
r 31
 
5.5%
s 27
 
4.8%
a 27
 
4.8%
h 24
 
4.3%
o 21
 
3.8%
E 20
 
3.6%
Other values (31) 224
40.1%
Common
ValueCountFrequency (%)
82
77.4%
, 7
 
6.6%
1 5
 
4.7%
) 3
 
2.8%
+ 3
 
2.8%
( 3
 
2.8%
4 1
 
0.9%
2 1
 
0.9%
5 1
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 657
98.8%
None 8
 
1.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
82
 
12.5%
i 58
 
8.8%
n 49
 
7.5%
e 42
 
6.4%
t 36
 
5.5%
r 31
 
4.7%
s 27
 
4.1%
a 27
 
4.1%
h 24
 
3.7%
o 21
 
3.2%
Other values (38) 260
39.6%
None
ValueCountFrequency (%)
ä 6
75.0%
ü 2
 
25.0%

Unnamed: 46
Text

Missing 

Distinct6
Distinct (%)100.0%
Missing186
Missing (%)96.9%
Memory size8.1 KiB
2025-11-23T13:48:05.433650image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length69
Median length49.5
Mean length47.833333
Min length25

Characters and Unicode

Total characters287
Distinct characters35
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)100.0%

Sample

1st rowPET hat Diszitis verkannt
2nd rowPET hat Beine nicht abgebildet, wo Eintrittspforte war
3rd rowPET hat keine Diszitis, intraop eitrig, Patho chron/Mibi unauffällig
4th rowEndokarditis nicht erkannt
5th rowPET hat zweite Höhe erkannt, MRT holo ergänzt
ValueCountFrequency (%)
pet 5
 
11.9%
hat 4
 
9.5%
diszitis 3
 
7.1%
erkannt 2
 
4.8%
nicht 2
 
4.8%
keine 2
 
4.8%
mrt 2
 
4.8%
wir 1
 
2.4%
und 1
 
2.4%
osteochondrose 1
 
2.4%
Other values (19) 19
45.2%
2025-11-23T13:48:05.651257image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
36
 
12.5%
i 29
 
10.1%
t 26
 
9.1%
n 20
 
7.0%
e 20
 
7.0%
a 16
 
5.6%
r 13
 
4.5%
h 12
 
4.2%
s 12
 
4.2%
o 12
 
4.2%
Other values (25) 91
31.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 210
73.2%
Space Separator 36
 
12.5%
Uppercase Letter 33
 
11.5%
Other Punctuation 8
 
2.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 29
13.8%
t 26
12.4%
n 20
9.5%
e 20
9.5%
a 16
 
7.6%
r 13
 
6.2%
h 12
 
5.7%
s 12
 
5.7%
o 12
 
5.7%
g 6
 
2.9%
Other values (13) 44
21.0%
Uppercase Letter
ValueCountFrequency (%)
P 7
21.2%
T 7
21.2%
E 7
21.2%
M 4
12.1%
D 3
9.1%
R 2
 
6.1%
B 1
 
3.0%
H 1
 
3.0%
O 1
 
3.0%
Other Punctuation
ValueCountFrequency (%)
, 6
75.0%
/ 2
 
25.0%
Space Separator
ValueCountFrequency (%)
36
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 243
84.7%
Common 44
 
15.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 29
 
11.9%
t 26
 
10.7%
n 20
 
8.2%
e 20
 
8.2%
a 16
 
6.6%
r 13
 
5.3%
h 12
 
4.9%
s 12
 
4.9%
o 12
 
4.9%
P 7
 
2.9%
Other values (22) 76
31.3%
Common
ValueCountFrequency (%)
36
81.8%
, 6
 
13.6%
/ 2
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 284
99.0%
None 3
 
1.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
36
12.7%
i 29
 
10.2%
t 26
 
9.2%
n 20
 
7.0%
e 20
 
7.0%
a 16
 
5.6%
r 13
 
4.6%
h 12
 
4.2%
s 12
 
4.2%
o 12
 
4.2%
Other values (23) 88
31.0%
None
ValueCountFrequency (%)
ä 2
66.7%
ö 1
33.3%

Interactions

2025-11-23T13:47:51.920787image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T13:47:51.752280image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T13:47:52.006977image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-23T13:47:51.836129image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-11-23T13:48:05.772577image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
1 = Fokussuche, 2 = Ausschluss/Nachweis Diszitis weil MRT unklar, 3 = MRT nicht möglich / Ersatz für MRT, 4 = VK, 5 = Materialschaden, Frage nach Infekt, 6 = Infekt im Labor ohne Fokus1 = lowgrade 2 = highgradeASAAuthor: (1= DM, 2=iv Drogen, 3=PAVK, 4=Cortisontherapie, 5=Zahnbehandlung, 6=Immunschwäche, 7=zn CTX, 8=Malignom, 9=Infiltrationen, 10=C2) RisikofaktorenAuthor: MRT: keine Diszitis, PET Diszitis spinal: overall, 0 = nicht übereinstimmend, 1= übereinstimmend, 2 = MRT unklar, 3 kein MRT, 4 unklarBWSBesserung 1 = komplett 2 =ja, aber nicht auf normal 3 tot 4 neues DefizitFokus abgeklärtHWSLWSNeurologie 1 = Paresen, 2 = vorbestehend, 3 = TetrapareseRevisionsOP 2 =kein InfektTE (at all)TE at sus focus 2 = vorOP spinal, 0 Fokus nicht dargestellt, 3 kein Fokus, 4 Fokus weg/saniert, 5 Fokus nicht gefundenUnnamed: 0add TEadd TE 1 = new focus 2=TE nicht relevant und tatsächlich nicht relevant 3 = nicht untersucht 4 = kein Fokus 5 = bekannter Fokus, schon behandelt 6 Tumor 0 no add TEageausgeheilt 2=NA 3=deadbiopsydiscitis in MRT = TE, 2 Frage diszitis b MRT unklar, 0 = n übereinstimmend, 3 kein MRT, 4 Ausschluss Diszitis, 5 Diszitis im MRT n erkannt, 6 neuer Nachweis Diszitishisto surgery 3 intermediär 0 negintraspinalnonspinal: overall 2 unklar 3 nicht abklärt 0 nicht übereinstimmend 4 ausgeheilt nach Behandlung 5 nicht abgebildetother spinal TEreason for PETsex (1F, 2M)unspez Fokus abgeklärt 0nein 1ja+neg 2ja+posunspez gewertetweitere
1 = Fokussuche, 2 = Ausschluss/Nachweis Diszitis weil MRT unklar, 3 = MRT nicht möglich / Ersatz für MRT, 4 = VK, 5 = Materialschaden, Frage nach Infekt, 6 = Infekt im Labor ohne Fokus1.0000.2550.0000.2540.4040.0000.2680.0000.1310.0000.0000.0000.4800.0000.0000.0600.0000.1100.0370.0000.6480.1550.0000.0000.3520.7300.0800.2110.0000.256
1 = lowgrade 2 = highgrade0.2551.0000.0000.1730.1810.0560.0000.0000.0000.0000.0000.0000.1820.0001.0000.1840.0000.0000.0720.0000.0910.1910.0000.0000.1140.5360.1040.0700.0000.000
ASA0.0000.0001.0000.1870.0000.1360.0000.0000.1460.1810.0620.0000.0000.0830.0000.0000.0000.0000.2830.0900.0000.2450.0680.0000.0000.0000.0560.0000.2240.000
Author: (1= DM, 2=iv Drogen, 3=PAVK, 4=Cortisontherapie, 5=Zahnbehandlung, 6=Immunschwäche, 7=zn CTX, 8=Malignom, 9=Infiltrationen, 10=C2) Risikofaktoren0.2540.1730.1871.0000.1760.0000.0000.0000.2670.0260.3430.0000.0000.0000.0000.0000.2310.0000.3830.0000.2590.0000.3030.0000.4630.2160.2640.4230.2020.324
Author: MRT: keine Diszitis, PET Diszitis spinal: overall, 0 = nicht übereinstimmend, 1= übereinstimmend, 2 = MRT unklar, 3 kein MRT, 4 unklar0.4040.1810.0000.1761.0000.0900.0000.0000.1300.0000.0000.0000.4940.0000.2200.0000.0000.1540.2260.0000.6210.2810.0930.0000.2720.3940.0590.0000.0000.000
BWS0.0000.0560.1360.0000.0901.0000.3380.1090.0450.3970.1790.1230.0000.0000.0000.0000.0000.0000.1010.0800.0000.0000.0000.0000.0000.0000.0000.0000.0000.067
Besserung 1 = komplett 2 =ja, aber nicht auf normal 3 tot 4 neues Defizit0.2680.0000.0000.0000.0000.3381.0000.0000.4630.3350.4520.0000.4400.0000.0000.0000.2310.1340.4560.1690.0000.2220.1490.2190.0000.2710.0850.3550.3550.000
Fokus abgeklärt0.0000.0000.0000.0000.0000.1090.0001.0000.1350.0000.0000.0000.0000.0590.0000.6360.3940.0380.0000.0000.0000.0000.0080.2200.0330.0000.0000.0000.0000.000
HWS0.1310.0000.1460.2670.1300.0450.4630.1351.0000.4470.3150.0000.0750.2060.0000.2150.2950.2170.0000.1420.1700.0000.1010.0000.2100.0000.0000.1960.2660.140
LWS0.0000.0000.1810.0260.0000.3970.3350.0000.4471.0000.0000.1330.0000.0000.0000.0000.1340.1940.0000.0590.1120.0000.0000.1150.0000.1250.0000.1140.0270.000
Neurologie 1 = Paresen, 2 = vorbestehend, 3 = Tetraparese0.0000.0000.0620.3430.0000.1790.4520.0000.3150.0001.0000.0000.1670.0000.3830.0000.0310.2330.0000.1570.0000.0000.0700.1500.0000.0000.0000.2580.2880.000
RevisionsOP 2 =kein Infekt0.0000.0000.0000.0000.0000.1230.0000.0000.0000.1330.0001.0000.0000.0000.0000.0000.0000.0000.2770.0000.0000.0000.0000.4040.0000.4160.0000.0000.0000.000
TE (at all)0.4800.1820.0000.0000.4940.0000.4400.0000.0750.0000.1670.0001.0000.1420.1570.2280.0000.0000.2780.0000.3580.4540.2410.0000.0000.3560.1160.0560.0000.000
TE at sus focus 2 = vorOP spinal, 0 Fokus nicht dargestellt, 3 kein Fokus, 4 Fokus weg/saniert, 5 Fokus nicht gefunden0.0000.0000.0830.0000.0000.0000.0000.0590.2060.0000.0000.0000.1421.0000.0000.2640.2840.0580.0000.0000.2800.0000.0350.4230.0940.0000.0000.1350.2930.246
Unnamed: 00.0001.0000.0000.0000.2200.0000.0000.0000.0000.0000.3830.0000.1570.0001.0000.0000.000-0.0210.0000.0000.2340.0000.0000.0000.0000.0000.0000.0000.0000.000
add TE0.0600.1840.0000.0000.0000.0000.0000.6360.2150.0000.0000.0000.2280.2640.0001.0000.9450.0000.1330.1380.0000.0520.0000.4220.0900.0000.0000.1560.2070.000
add TE 1 = new focus 2=TE nicht relevant und tatsächlich nicht relevant 3 = nicht untersucht 4 = kein Fokus 5 = bekannter Fokus, schon behandelt 6 Tumor 0 no add TE0.0000.0000.0000.2310.0000.0000.2310.3940.2950.1340.0310.0000.0000.2840.0000.9451.0000.1750.0000.0000.0000.0000.0000.5250.0000.0000.0000.3690.2100.000
age0.1100.0000.0000.0000.1540.0000.1340.0380.2170.1940.2330.0000.0000.058-0.0210.0000.1751.0000.0400.0000.0470.0820.0000.2060.0000.0900.0000.0000.0000.000
ausgeheilt 2=NA 3=dead0.0370.0720.2830.3830.2260.1010.4560.0000.0000.0000.0000.2770.2780.0000.0000.1330.0000.0401.0000.0000.1900.4250.0000.0000.0000.0640.0000.0000.0000.000
biopsy0.0000.0000.0900.0000.0000.0800.1690.0000.1420.0590.1570.0000.0000.0000.0000.1380.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
discitis in MRT = TE, 2 Frage diszitis b MRT unklar, 0 = n übereinstimmend, 3 kein MRT, 4 Ausschluss Diszitis, 5 Diszitis im MRT n erkannt, 6 neuer Nachweis Diszitis0.6480.0910.0000.2590.6210.0000.0000.0000.1700.1120.0000.0000.3580.2800.2340.0000.0000.0470.1900.0001.0000.2620.0000.0000.4050.5280.0000.1510.1720.202
histo surgery 3 intermediär 0 neg0.1550.1910.2450.0000.2810.0000.2220.0000.0000.0000.0000.0000.4540.0000.0000.0520.0000.0820.4250.0000.2621.0000.1570.0000.0000.0000.2090.2100.3530.307
intraspinal0.0000.0000.0680.3030.0930.0000.1490.0080.1010.0000.0700.0000.2410.0350.0000.0000.0000.0000.0000.0000.0000.1571.0000.0000.0000.0000.0000.0750.3530.000
nonspinal: overall 2 unklar 3 nicht abklärt 0 nicht übereinstimmend 4 ausgeheilt nach Behandlung 5 nicht abgebildet0.0000.0000.0000.0000.0000.0000.2190.2200.0000.1150.1500.4040.0000.4230.0000.4220.5250.2060.0000.0000.0000.0000.0001.0000.0000.0000.0000.1210.0000.000
other spinal TE0.3520.1140.0000.4630.2720.0000.0000.0330.2100.0000.0000.0000.0000.0940.0000.0900.0000.0000.0000.0000.4050.0000.0000.0001.0000.3910.0000.3950.4000.216
reason for PET0.7300.5360.0000.2160.3940.0000.2710.0000.0000.1250.0000.4160.3560.0000.0000.0000.0000.0900.0640.0000.5280.0000.0000.0000.3911.0000.0000.2580.0000.000
sex (1F, 2M)0.0800.1040.0560.2640.0590.0000.0850.0000.0000.0000.0000.0000.1160.0000.0000.0000.0000.0000.0000.0000.0000.2090.0000.0000.0000.0001.0000.0000.0000.000
unspez Fokus abgeklärt 0nein 1ja+neg 2ja+pos0.2110.0700.0000.4230.0000.0000.3550.0000.1960.1140.2580.0000.0560.1350.0000.1560.3690.0000.0000.0000.1510.2100.0750.1210.3950.2580.0001.0000.7940.305
unspez gewertet0.0000.0000.2240.2020.0000.0000.3550.0000.2660.0270.2880.0000.0000.2930.0000.2070.2100.0000.0000.0000.1720.3530.3530.0000.4000.0000.0000.7941.0000.472
weitere0.2560.0000.0000.3240.0000.0670.0000.0000.1400.0000.0000.0000.0000.2460.0000.0000.0000.0000.0000.0000.2020.3070.0000.0000.2160.0000.0000.3050.4721.000

Missing values

2025-11-23T13:47:52.372277image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-11-23T13:47:52.795079image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-11-23T13:47:53.226020image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Unnamed: 0nameDOBageFokus abgeklärtunspez gewertetunspez Fokus abgeklärt 0nein 1ja+neg 2ja+posweitereThrombussex (1F, 2M)DiagnoseLWSBWSHWSintraspinalbiopsyOPDOAsurgery datedate of PETmibi surgery 0 = neg1 = lowgrade 2 = highgradehisto surgery 3 intermediär 0 negmibi otherCRP initialPETCT TETE (at all)discitis in MRT = TE, 2 Frage diszitis b MRT unklar, 0 = n übereinstimmend, 3 kein MRT, 4 Ausschluss Diszitis, 5 Diszitis im MRT n erkannt, 6 neuer Nachweis Diszitisother spinal TEAuthor: MRT: keine Diszitis, PET Diszitis spinal: overall, 0 = nicht übereinstimmend, 1= übereinstimmend, 2 = MRT unklar, 3 kein MRT, 4 unklarinitialer Fokusneuer Fokus nach PETTE at sus focus 2 = vorOP spinal, 0 Fokus nicht dargestellt, 3 kein Fokus, 4 Fokus weg/saniert, 5 Fokus nicht gefundenadd TEadd TE 1 = new focus 2=TE nicht relevant und tatsächlich nicht relevant 3 = nicht untersucht 4 = kein Fokus 5 = bekannter Fokus, schon behandelt 6 Tumor 0 no add TEnonspinal: overall 2 unklar 3 nicht abklärt 0 nicht übereinstimmend 4 ausgeheilt nach Behandlung 5 nicht abgebildetreason for PET1 = Fokussuche, 2 = Ausschluss/Nachweis Diszitis weil MRT unklar, 3 = MRT nicht möglich / Ersatz für MRT, 4 = VK, 5 = Materialschaden, Frage nach Infekt, 6 = Infekt im Labor ohne FokusAuthor: (1= DM, 2=iv Drogen, 3=PAVK, 4=Cortisontherapie, 5=Zahnbehandlung, 6=Immunschwäche, 7=zn CTX, 8=Malignom, 9=Infiltrationen, 10=C2) RisikofaktorenRevisionsOP 2 =kein InfektASAausgeheilt 2=NA 3=deadNeurologie 1 = Paresen, 2 = vorbestehend, 3 = TetrapareseBesserung 1 = komplett 2 =ja, aber nicht auf normal 3 tot 4 neues Defizit(1= DM, 2=iv Drogen, 3=PAVK, 4=Cortisontherapie, 5=Zahnbehandlung, 6=Immunschwäche, 7=zn CTX, 8=Malignom, 9=Infiltrationen, 10=C2, 11=Parkinson, 12 = Niereninsuffizienz)Unnamed: 45Unnamed: 46
01069341.0Rankel, Christine07.11.196556.0----NaN1.0Spondylodiszitis TH5/60.01.00.00.00Solera TH4-5-6-7 + Deko+BSF-Ausräumung04.04.202230.05.2022NaNStreptococcus dysgalactiae, Staphylococcus capitis21NaN3,6NaN1.0101keinkein30.001Fokussuche11Author: 13.01NaNNaNNaNNaNNaN
11121310.0Mentzel, Frank17.09.195467.0---Ulcus, bekanntNaN2.0Diszitis L4/51.00.00.00.00Solera LW2-3-S2-IA, ALIF L4/518.02.202203.03.202222.02.2022Staphylococcus epidermidis, Staphylococcus warneri11-11,2NaN1.0101Author: 6 Wo sVorOP, Ulcuskein20.001Fokussuche1423.01NaNNaNNaNein PET Onko zusätzlich davorNaN
31187375.0Vial, Renee10.03.195072.0---BronchialCANaN1.0Spondylodiszitis L4/5/S1+Empyem1.00.00.01.00Solera L4-5-S1+Deko, ALIF L4/5, 5/S121.12.202222.12.2022NaNStaphylococcus epidermidis11-2,9NaN1.0101Author: 7 W sVorOPkein20.001Fokussuche17,802.01NaNNaNNaNein PET Onko zusätzlich davorNaN
41202379.0Mayr, Daniela08.05.198438.0---Zn PankreatitisNaN1.0Ausschluss Diszitis1.01.00.00.01-21.10.2022-02.11.2022not doneNaNnot done-12NaN0.0202Polytox vor 20 a, Zn Diszitiskein30.001Ausschluss Diszitis2203.0xNaNNaNNaNNaNNaN
51227623.0Zellner, Leonhard12.09.194775.0----1.02.0WHST1.00.00.00.00Verlängerung L2-3-4-5-S1-S2IA31.07.202301.08.2023NaNStaphylococcus epidermidis11-13,7NaN1.010kein Handlungsbedarf bei einliegender SI-Schraube 1sVorOPkein20.001Fokussuche1002.01NaNNaNNaNNaNNaN
61266244.0Hinrichs, Holger17.04.195369.0-Colitis0-1.02.0Spondylodiszitis LW2/3 und LW3/4, Z. n. Solera TH 12-L1-L4-L5+WKE1.00.00.00.00ME, WKE L2-4, Solera TH9-10-11-L5-SIA09.08.202211.08.2022NaNSerratia marcescens, Serratia nematodiphilia21-5,9NaN1.0101Zn Diszitis nach Erysipel US rekein (Sinusitis, Colitis nicht bestätigt)21.0Author: Colitis als unspez gewertet (unklar ob klinisch iV kontrolliert), Sinusitis ohne TE, kein Fokus 2Author: Sinusitis ja (ohne TE), aber kein Fokus 1Fokussuche1104.02NaNNaNNaNNaNNaN
71267203.0Gamon, Ingrid15.06.193982.0-HSM1-NaN1.0Schraubenlockerung L4 bds. b Z. n. Cosmic L4-5-S11.00.00.00.00ME02.03.202202.03.202207.03.2022001-0,3NaN1.0101sVorOPkein20.001Fokussuche1402.01NaNNaNNaNKnochenPET danachNaN
81275982.0Ghellere, Renato18.06.193488.01---NaN2.0Spondylodiszitis L1-L21.00.00.00.00OSS17.12.202202.01.202322.12.2022Staphylococcus aureus22-17,3NaN1.0000keinPneumonie (r), Kolitis (r)31.0Author: Colitis als relevant eingestuft, aber nicht bestätigt 33Fokussuche1103.02NaNNaNNaNNaNPET hat Diszitis verkannt
91369728.0Meier-Meitinger, Maximilian10.03.198340.0----NaN2.0Spondylodiszitis L5/S11.00.00.00.01Solera L5-S1+Deko, ALIF17.05.202322.05.202301.06.2023Staphylococcus aureus, Cutibacterium (Propionibacterium) acnes21-13,3NaN1.0101Hautverletzung mit rostigem Nagel vor 2 Wochenkein50.00Author: Extremitäten nicht mitabgebildet 5Fokussuche1002.01NaNNaNNaNNaNPET hat Beine nicht abgebildet, wo Eintrittspforte war
101381129.0Achatz, Heidi04.09.194181.0-kleine Gelenke, ae rheumatologisch0NeurinomNaN1.0Spondylodiszitis C5/60.00.01.00.00ACDF C5/603.12.202207.12.202215.12.202200Author: intermediär (chron granulierend) 3-16,4NaN1.0000Z.n. Urosepsiskein40.004Fokussuche17, 803.01NaNNaNNaNDD RheumaPET hat keine Diszitis, intraop eitrig, Patho chron/Mibi unauffällig
Unnamed: 0nameDOBageFokus abgeklärtunspez gewertetunspez Fokus abgeklärt 0nein 1ja+neg 2ja+posweitereThrombussex (1F, 2M)DiagnoseLWSBWSHWSintraspinalbiopsyOPDOAsurgery datedate of PETmibi surgery 0 = neg1 = lowgrade 2 = highgradehisto surgery 3 intermediär 0 negmibi otherCRP initialPETCT TETE (at all)discitis in MRT = TE, 2 Frage diszitis b MRT unklar, 0 = n übereinstimmend, 3 kein MRT, 4 Ausschluss Diszitis, 5 Diszitis im MRT n erkannt, 6 neuer Nachweis Diszitisother spinal TEAuthor: MRT: keine Diszitis, PET Diszitis spinal: overall, 0 = nicht übereinstimmend, 1= übereinstimmend, 2 = MRT unklar, 3 kein MRT, 4 unklarinitialer Fokusneuer Fokus nach PETTE at sus focus 2 = vorOP spinal, 0 Fokus nicht dargestellt, 3 kein Fokus, 4 Fokus weg/saniert, 5 Fokus nicht gefundenadd TEadd TE 1 = new focus 2=TE nicht relevant und tatsächlich nicht relevant 3 = nicht untersucht 4 = kein Fokus 5 = bekannter Fokus, schon behandelt 6 Tumor 0 no add TEnonspinal: overall 2 unklar 3 nicht abklärt 0 nicht übereinstimmend 4 ausgeheilt nach Behandlung 5 nicht abgebildetreason for PET1 = Fokussuche, 2 = Ausschluss/Nachweis Diszitis weil MRT unklar, 3 = MRT nicht möglich / Ersatz für MRT, 4 = VK, 5 = Materialschaden, Frage nach Infekt, 6 = Infekt im Labor ohne FokusAuthor: (1= DM, 2=iv Drogen, 3=PAVK, 4=Cortisontherapie, 5=Zahnbehandlung, 6=Immunschwäche, 7=zn CTX, 8=Malignom, 9=Infiltrationen, 10=C2) RisikofaktorenRevisionsOP 2 =kein InfektASAausgeheilt 2=NA 3=deadNeurologie 1 = Paresen, 2 = vorbestehend, 3 = TetrapareseBesserung 1 = komplett 2 =ja, aber nicht auf normal 3 tot 4 neues Defizit(1= DM, 2=iv Drogen, 3=PAVK, 4=Cortisontherapie, 5=Zahnbehandlung, 6=Immunschwäche, 7=zn CTX, 8=Malignom, 9=Infiltrationen, 10=C2, 11=Parkinson, 12 = Niereninsuffizienz)Unnamed: 45Unnamed: 46
2012747133.0Girgis, Raafat Riad Fayek21.05.195366.0Author: abgelehnt 0---NaN2.0Spondylodiszitis BW1-2-30.01.00.00.00Neon C6-7-Th4-5, Laminektomie C7, Th1-3 +WK BW1-3 ex, zweiter Schritt WKE/Fibula20.01.202022.01.2020 29.01.202006.02.2020Pseudomonas21Pseudomonas )BK), STAU (BK), Enterobacter vloacae (STAU)5,2NaN1.0101keinkein (Colon nicht untersucht)31.032Fokussuche11, 303.011.04NaNNaNNaN
2022716417.0Huber, Hans-Jürgen21.04.196455.0----NaN2.0Spondylitis BW10/11 DD Tumor0.01.00.00.01Solera Th9-10-11-1212.09.201907.11.201917.09.2019Staphylococcus saccharolyticus, Cutibacterium (Propionibacterium) acnes01STAU, Staphylococcus capitis (BK)4,2NaN1.0101Z.n. Appendizitiskein40.001Fokussuche, Nachweis Diszitis1, 2413.010.0xNaNNaNNaN
2032702692.0Schwarz, Ursula15.09.194672.01---NaN2.0Spondylodiszitis BW12/L10.01.00.00.01Solera BWK10-11-L2-3, WKE BWK12/l112.07.201916.07.2019 01.08.201913.08.2019E. coli21E. coli5,3NaN1.0101Z.n. UrosepsisPleuraempyem41.011Fokussuche1314.000.0xNaNNaNNaN
20421655353.0Karasova, Simona10.04.199424.0----NaN1.0Ausschluss Infektfokus, Zn OP L2 + L3-Berstungsfraktur Fraktur, Os Sacrum Fraktur1.00.00.00.001. 3D Röntgen navigierte Stabilisierung von dorsal Th 12-L1-L4-L5 (Solera), Dekompression via Laminektomie LW 2-3 Teillaminektomie LW4, Versorgung eines traumatischen Duralecks Höhe L3/4 bds 2. Abbau Fixateur externe Schraubenosteosynthese Talus (3x kanülierte CCS-Schraube) Schraubenosteosynthese Innenknöchel winkelstabile Plattenosteosynthese Pilon tibiale (Arthrex distal. Tibiaplate sowie distale Fibula-1/3-Rohrplatte) 3. Wundrevision LWS von dorsal 4. WK-Ersatz LW2 und 3 mit Titan Cage ( Obelisc, Ullrich MEdical ) vialinks retroperitonialer Zugang von links 5. ME Fixateur externe rechts (Unfallchirurgie)10.01.201910.01.2019 21.01.2019 22.01.2019 28.01.2019 04.02.201912.02.2019not doneNaNnot done-2,8NaN0.0303keinkein30.001Ausschluss Diszitis2003.011.0xNaNNaNNaN
2051110893.0Möller, Gerhard08.06.194672.0Author: abgelehnt 0--ED ProstataCANaN2.0Spondylodiszitis LWK5/SWK11.00.00.01.01Solera L4-5-S1, Laminektomie LW4,5, Facettenektomie LW4/5 beidseits, Ausräumung epidurales Empyem und Probeentnahme LWK5, Diskektomie LWK5/SW1 und interkorporelle Spondylodese mit autologem Knochen, CT-navigierte Verlängerung der bestehenden Solera Stabilisierung auf LWK3 beidseits, Dekompression über Laminektomie LW3, Neurolyse der L5 Wurzel15.12.201828.12.2018 02.01.2019 11.01.2019 08.06.202008.01.2019Staphylococcus hominis, Staphylococcus epidermidis11-5,2NaN1.0212keinDiszitis31.033Fokussuche, Nachweis Diszitis1, 2823.020.0xNachweis Diszitis, ED ProstataCA, 1x OnkoPETNaNNaN
2062622568.0Kett, Margarete09.02.193187.0----NaN1.0Spondylodiszitis BW12-LW1-LW1/2 bei Z. n. Kyphoplastie bei Z. n. mehrfach Kyphoplastien in LWK4-3-2 auswärts1.01.00.00.01Icotec BWK11,12- LWK2,3,4,5. Laminektomie LWK1,2,3 teilweise LWK4. Foraminotomie LW1/2, LW3/4. WKE LWK106.09.201814.09.2018 01.10.2018 04.11.201828.09.2018STAU21-2,5NaN1.0101keinkein20.001Fokussuche1303.020.041x herz SpectNaNNaN
2072592895.0Wittmann, Peter13.02.195959.0----NaN2.0Serom thorakal bei Z.n. Spondylodiszitis Th7/8 b Z.n. Disztis mit epiduralem Empyem, Zn Solera Th6-7-9-10 + Deko+ Ausräumung BSF0.01.00.01.01WKE TH8 + Teil WK710.08.201816.08.201814.08.2018STAU21NaN22,4NaN1.0101Z.n. Diszitiskein20.001Fokussuche1103.022.0xNaNNaNNaN
2082604561.0Gruenwald, Konrad22.06.193484.0----NaN2.0Spondylodiszitis BWK12/LWK1, Zn Deko L3/4/51.01.00.00.001. Perkutane CT-gesteuerte dorsale Stabilisierung (Solera) BWK11-12-LWK1-2 2. Transthorakaler Zugang von rechts, Wirbelkörperersatz BWK12/LWK1, GENTA-COLL27.06.201802.07.2018 03.07.201809.07.2018Parvimonas micra2101,3NaN1.0101sVorOP (andere Höhe)kein2./30.001Fokussuche1003.010.0xNaNNaNNaN
2091559145.0Meier, Franz17.07.193481.01---NaN2.0Spondylodiszitis HW7/BW1, Zn ACDF HW4/50.01.01.00.00ACDF+Platte HW7/BW118.04.201625.04.201628.04.2016Staphylococcus agalacticae21Staphylococcus agalacticae (BK, Knie)8,2NaN1.0101sVorOPKnie11.011Fokussuche1103.020.0xNaNNaNNaN
2102344995.0Duna, Georg12.12.193283.0---Adenom ColonNaN2.0Infektion LW4/5 und LW5/SW1 + Schraubenlockerung LWK4 b Z.n. Revisionsspondylodese 2015 bei Lockerung, Z.n. dorsoventraler Stabilisierung LW4-SW1 + Deko bei Stenose, Z.n. mehrfacher Deko LWS1.00.00.00.01Revision mit Explantation der Schrauben LW4, Evakuation Pus BSF L4/5/S1, Viper L3-5-S221.03.201622.03.201623.03.2016Staphylococcus agalacticae21Staphylococcus agalacticae (BK, Knie)1,8NaN1.0101sVorOPEndokarditis21.011Fokussuche1802.020.0x1x herz SpectNaNNaN